{"access":{"advertiser_pricing_url":"https://aidevboard.com/pricing","catalog_url":"https://aidevboard.com/api/v1/catalog","description":"Public read endpoints are open and free. API keys are optional for stable agent identity and keyed hourly throttling.","docs_url":"https://aidevboard.com/docs","mode":"open","register_url":"https://aidevboard.com/api/v1/register"},"degraded":false,"estimated":false,"has_next":true,"jobs":[{"id":"f8c6c621-b459-40f6-b41d-0baa191734ff","company_id":"a0000000-0000-0000-0000-000000000001","title":"Research Lead, Training Insights","slug":"research-lead-training-insights-6091f430","description":"About Anthropic \n Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n About the role \n As a Research Lead on the Training Insights team, you'll develop the strategy for, and lead execution on, how we measure and characterize model capabilities across training and deployment. This is a hands-on leadership role: you'll drive original research into new evaluation methodologies while leading a small team of researchers and research engineers doing the same.\n Your work will span the full lifecycle of model development. You'll research and build new long-horizon evaluations that test the boundaries of what our models can achieve, develop novel approaches to measuring emerging capabilities, and deepen our understanding of how those capabilities develop — both during production RL training and after. You'll also take a cross-organizational view, working across Reinforcement Learning, Pretraining, Inference, Product, Alignment, Safeguards, and other teams to map the landscape of model evaluations at Anthropic and identify critical gaps in coverage.\n This role carries significant visibility and impact. You'll help shape the evaluation narrative for model releases, contributing directly to how Anthropic communicates about its models to both internal and external audiences. Done well, you will change how the industry measures and understands model capabilities, significantly furthering our safety mission.  \n Responsibilities:  \n \n Build new novel and long-horizon evaluations\n Develop novel measurement approaches for understanding how model capabilities emerge and evolve during RL training\n Lead strategic evaluation coverage across the company\n Shape the evaluation narrative for model releases\n Lead and mentor a small team of researchers and research engineers, setting research direction and fostering a culture of rigorous, creative research\n Design evaluation frameworks that balance scientific rigor with the practical demands of production training schedules\n Build and maintain relationships across Anthropic's research organization to ensure evaluation insights inform training and deployment decisions\n Contribute to the broader research community through publications, open-source contributions, or external engagement on evaluation best practices\n \n You may be a good fit if you:  \n \n Have significant experience designing and running evaluations for large language models or similar complex ML systems\n Have led technical projects or teams, either formally or through sustained ownership of critical research directions\n Are equally comfortable designing experiments and writing code—you can move between research and implementation fluidly\n Think strategically about what to measure and why, not just how to measure it\n Can synthesize information across multiple teams and workstreams to form a coherent picture of model capabilities\n Communicate complex technical findings clearly to both technical and non-technical audiences\n Are results-oriented and thrive in fast-paced environments where priorities shift based on research findings\n Care deeply about AI safety and want your work to directly influence how capable AI systems are developed and deployed\n \n Strong candidates may also have:  \n \n Experience building evaluations for long-horizon or agentic tasks\n Deep familiarity with Reinforcement Learning training dynamics and how model behavior changes during training\n Published research in machine learning evaluation, benchmarking, or related areas\n Experience with safety evaluation frameworks and red teaming methodologies\n Background in psychometrics, experimental psychology, or other measurement-focused disciplines\n A track record of communicating evaluation results to inform high-stakes decisions about model development or deployment\n Experience managing or mentoring researchers and engineers\n \n Representative projects:  \n \n Designing and implementing a suite of long-horizon evaluations that test model capabilities on tasks requiring sustained reasoning, planning, and tool use over extended interactions\n Building systems to track capability development across RL training checkpoints, surfacing insights about when and how specific capabilities emerge\n Conducting a cross-org audit of evaluation coverage, identifying blind spots, and prioritizing new evaluations to fill critical gaps across Pretraining, RL, Inference, and Product\n Developing the evaluation methodology and narrative for a major model release, working with research leads and communications to clearly characterize model capabilities and limitations\n Researching and prototyping novel evaluation approaches for capabilities that are difficult to measure with existing benchmarks\n Leading a team","salary_min":850000,"salary_max":850000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["agents","llm","alignment","reinforcement-learning","pre-training","search","research"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5139654008","is_featured":true,"is_sticky":false,"status":"active","published_at":"2026-03-06T17:15:29Z","expires_at":"2026-08-14T14:00:30.363031Z","created_at":"2026-04-13T09:36:01.625992Z","updated_at":"2026-07-15T14:00:30.486844Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/f8c6c621-b459-40f6-b41d-0baa191734ff"},{"id":"9cf703e4-28cb-47a7-9151-d26f9745f43d","company_id":"74257563-5513-4a8d-a0f7-01f00c59aed6","title":"Senior Machine Learning Engineer, Relevance and Personalization (Query Intelligence)","slug":"senior-machine-learning-engineer-relevance-and-personalization-query-intelligence-b6fdeb9a","description":"Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way. \n The Community You Will Join: \n The Relevance and Personalization team at Airbnb is responsible for search and recommendation across the entire Airbnb digital platform. In this role you'll focus on query intelligence, the front door of search working on critical, impactful projects that turn what a guest types, taps, or says into a precise understanding of their intent, spanning autocomplete and smart compose, query tagging, query expansion, and intent modeling across Stays, Experiences, and Services.\n The Difference You Will Make: \n Query understanding is where every search begins, and it directly shapes retrieval, ranking, and ultimately the perfect match between guests and hosts. We build cutting-edge AI technologies across the end-to-end search ranking product stack w.r.t. data pipelines, feature and model innovations, serving and experimentation efficiency, leveraging rich signals from various types of data (structured, sequential, image, text, etc) and increasingly large language models at Airbnb. You'll build the models that parse free-form and natural-language multimodal queries, extract entities and location context, classify intent, and anticipate what guests want before they finish typing. We collaborate closely with teams across Airbnb to develop the ranking solutions and support a healthy marketplace for hosts and guests to further Airbnb's mission of creating a world where people can Belong Anywhere. Some past publications from the team can be found here: https://sites.google.com/view/airbnb-relevance-publications/home \n A Typical Day:  \n \n Work with large scale structured and unstructured data, build and continuously improve cutting edge Machine Learning models for Airbnb product, business and operational use cases, with a focus on query understanding.\n Develop query understanding capabilities — autocomplete and smart compose, query tagging (sequence tagging / NER), query expansion, and query/user intent modeling — and natural-language (\"search in your own words\") search experiences powered by modern NLP and LLMs.\n Work collaboratively with cross-functional partners including software engineers, product managers, operations and data scientists, identify opportunities for business impact, understand, refine, and prioritize requirements for machine learning models, drive engineering decisions, and quantify impact.\n Hands-on develop, productionize, and operate Machine Learning models and pipelines at scale, including both batch and real-time use cases.\n Leverage third-party and in-house Machine Learning tools \u0026 infrastructure to develop reusable, highly differentiating and high-performing Machine Learning systems, enable fast model development, low-latency serving and ease of model quality upkeep.\n Example projects include: smart compose and language generation for search, LLM-based sequence taggers, LLM-driven query/location expansion, intent classification, and user-intent sequence modeling.\n \n Your Expertise: \n \n 5+ years of industry experience in applied Machine Learning, inclusive MS or PhD in relevant fields.\n Strong programming (Scala / Python / Java / C++ or equivalent) and data engineering skills.\n Deep understanding of Machine Learning best practices (eg. training/serving skew minimization, A/B test, feature engineering, feature/model selection), algorithms (eg. neural networks/deep learning, optimization) and domains (eg. natural language processing, personalization, search and recommendation, marketplace optimization).\n Experience with 3 or more of these technologies: Tensorflow, PyTorch, Kubernetes, Spark, Airflow (or equivalent), Kafka (or equivalent), data warehouse (eg. Hive).\n Industry experience building end-to-end Machine Learning models.\n Experience applying large language models and modern NLP — e.g., sequence tagging/NER, text generation, intent classification, or embedding/representation learning.\n Familiarity with building natural-language, AI-native and agentic search experiences is a plus.\n Exposure to architectural patterns of large, high-scale software applications (e.g., well-designed APIs, high volume data pipelines, efficient algorithms, models).\n \n Your Location: \n This position is US - Remote Eligible. The role may include occasional work at an Airbnb office or attendance at offsites, as agreed to with your manager. While the position is Remote Eligible, you must live in a state where Airbnb, Inc. has a registered entity. Click here for the up-to-date list of excluded states. This list is continuously evolving, so please check back with us if the state you live in is on the exclusion list. If your po","salary_min":200000,"salary_max":235000,"location":"United States","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["llm","generative-ai","data-pipeline","agents","search","nlp","pytorch","tensorflow"],"apply_url":"https://careers.airbnb.com/positions/8065789?gh_jid=8065789","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-14T23:54:51Z","expires_at":"2026-08-14T14:11:22.875202Z","created_at":"2026-07-15T14:11:23.002744Z","updated_at":"2026-07-15T14:11:23.002744Z","company_name":"Airbnb","company_slug":"airbnb","company_logo_url":"https://www.google.com/s2/favicons?domain=airbnb.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/9cf703e4-28cb-47a7-9151-d26f9745f43d"},{"id":"66be6f1d-738c-4b9b-b07d-4cae69e7b29d","company_id":"a0000000-0000-0000-0000-000000000003","title":"Senior Machine Learning Engineer, Agent Oversight","slug":"senior-machine-learning-engineer-agent-oversight-774633fc","description":"About Scale\n Scale’s mission is to develop reliable AI systems for the world’s most important decisions. As the leading AI data foundry, we provide the high-quality data and full-stack technologies that power the world’s most advanced models — fueling breakthroughs in generative AI, defense, and autonomous vehicles. We partner with leading enterprises and governments to bring AI into production that performs when it matters most, combining rigorous evaluation with full-stack deployment so our customers can build AI they can trust.\n About the Team\n Applied Intelligence Systems team is part of the Scale Generative AI Platform (SGP), focused on pushing the frontier of what agentic applications can do across diverse enterprise and government use cases. We build the infrastructure and tooling that power agentic AI in production, paired with applied ML research, design, and evaluation to ensure these systems perform reliably at the scale our customers demand. We’re growing fast, with increasing traction across both commercial and public sector customers, and we’re just getting started — this team will define what dependable, production-grade agentic AI looks like.\n About the Role\n As a Machine Learning Engineer on Agent Oversight, you will drive the end-to-end lifecycle that ensures our production agents perform reliably and improve over time. This includes building observability tools, designing robust evaluation frameworks, and developing improvement loops. Whether scaling infrastructure or researching new improvement methods, you will navigate the entire ML loop while maintaining rigorous technical standards.\n You will:\n \n Build or contribute to observability into agent behavior in production — the signals and instrumentation needed to actually see what an agent is doing, not just whether it succeeded or failed\n Design evaluation methodologies and metrics for agentic applications, and work with the platform to make them run automatically, at scale, across different customer use cases, not just as one-off analyses\n Build, ship, and own ML systems that detect drift, anomalies, or misalignment in production agent behavior — from first prototype through running reliably at scale\n Design and run rigorous experiments to validate model and agent performance improvements before they ship\n Work alongside software engineers on the platform where your work intersects with broader infrastructure — but you’re expected to take your own work from idea to production, not hand it off\n Collaborate closely with product managers, customers, data annotators, Forward Deployed Engineers, and other engineering teams to translate enterprise and government requirements into robust platform capabilities\n Depending on focus, contribute to novel methods and approaches that push the state of the art for agent evaluation and improvement, or focus on building ML systems that hold up reliably at scale in production\n \n Requirements:\n \n 5+ years of experience as an ML engineer or applied scientist, ideally on a production ML or LLM-powered system — not just consuming a third-party ML API within a feature\n Strong grounding in  at least two  of the following:\n \n Building or scaling evaluation, monitoring, or continuous-learning infrastructure for ML/agentic systems\n Design experience for agent systems (architecture, orchestration, tool use)\n Developing new methods, reward models, or model training/fine-tuning approaches\n \n Hands-on experience with LLMs and agent architectures — tool use, planning, multi-agent orchestration\n Comfortable partnering with software engineers to productionize research and experimental work, not just deliver a one-off analysis\n Rigorous approach to experimentation: clear hypotheses, real statistical grounding, and results that hold up under scrutiny\n Track record of collaborating across functions (Product, Forward Deployed Engineering, etc.) to navigate ambiguous requirements and bring them to production\n Gives direct, substantive feedback on designs and code, and takes it the same way — and mentors others as they grow\n \n Nice to have:\n \n Experience building or contributing to RLHF, SFT, or other fine-tuning/RL workflows, reward modeling, or verifiable-reward systems\n Experience with model or systems optimization (e.g., latency, cost, or inference efficiency)\n Published research, open-source contributions, or patents in agentic systems, LLMs, or applied ML\n Experience working in regulated or enterprise contexts\n Track record of taking a novel method from prototype to something running reliably in production, navigating ambiguity along the way\n Experience reviewing others’ technical designs or mentoring engineers at a senior/staff level\n Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career level","salary_min":216000,"salary_max":270000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["generative-ai","llm","fine-tuning","agents","reinforcement-learning","autonomous-vehicles","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/scaleai/jobs/4714527005","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-14T20:14:32Z","expires_at":"2026-08-14T14:01:47.147912Z","created_at":"2026-07-15T14:01:47.280877Z","updated_at":"2026-07-15T14:01:47.280877Z","company_name":"Scale AI","company_slug":"scale-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=scale.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/66be6f1d-738c-4b9b-b07d-4cae69e7b29d"},{"id":"96c4b57f-c214-4de0-829c-cda4957c7a17","company_id":"a0000000-0000-0000-0000-000000000003","title":"Senior Software Engineer, Agent Oversight","slug":"senior-software-engineer-agent-oversight-a8682235","description":"About Scale\n Scale’s mission is to develop reliable AI systems for the world’s most important decisions. As the leading AI data foundry, we provide the high-quality data and full-stack technologies that power the world’s most advanced models — fueling breakthroughs in generative AI, defense, and autonomous vehicles. We partner with leading enterprises and governments to bring AI into production that performs when it matters most, combining rigorous evaluation with full-stack deployment so our customers can build AI they can trust.\n About the Team\n Applied Intelligence Systems team is part of the Scale Generative AI Platform (SGP), focused on pushing the frontier of what agentic applications can do across diverse enterprise and government use cases. We build the infrastructure and tooling that power Agentic AI in production, paired with applied ML research, design, and evaluation to ensure these systems perform reliably at the scale our customers demand. We’re growing fast, with increasing traction across both commercial and public sector customers, and we’re just getting started — this team will define what dependable, production-grade agentic AI looks like.\n About the Role\n As a Software Engineer on Agent Oversight, you will build the platform infrastructure that lets our production agents be observed, evaluated, and improved at scale. This includes building observability tooling, evaluation harnesses, and the pipelines that connect them to improvement loops. Whether building foundational infrastructure or partnering closely with ML engineers on production workflows, you will own your systems end-to-end while maintaining rigorous technical standards.\n You will:\n \n Design and build core platform capabilities for deploying, monitoring, and evaluating agentic applications in production\n Build reliable APIs and data pipelines that capture agent telemetry, evaluation signals, and performance metrics at scale\n Work alongside ML engineers where platform work intersects with evaluation or improvement systems — bringing enough ML fluency to reason about model behavior, evaluation quality, and improvement loops while owning the software systems that make those workflows reliable\n Own the reliability, scalability, and observability of platform components serving multiple concurrent enterprise and government customers\n Work cross-functionally with product, forward deployed engineering, and customers to translate real-world deployment requirements into platform features\n Build features end-to-end: system design, implementation, debugging, and testing\n Participate in high-velocity experimentation to validate platform capabilities against real customer usage\n \n Requirements:\n \n 4+ years of professional software engineering experience, with strong fundamentals in backend/distributed systems, APIs, and data pipeline design\n Hands-on experience building production software for ML/LLM-powered products or platforms, such as evaluation systems, observability/monitoring, experimentation infrastructure, agent runtimes, model-serving-adjacent services, or telemetry/data pipelines\n Working knowledge of how LLM or ML systems behave in production: evaluation signals, failure modes, prompt/tool-calling workflows, experiment results, data quality issues, and the tradeoffs between offline evals and live customer behavior\n Experience partnering closely with ML engineers or applied researchers to turn prototypes, eval loops, or model-improvement workflows into reliable platform capabilities, without needing to own model training, modeling strategy, or research direction\n Experience building infrastructure or platforms that other engineering teams build on top of (internal platform, developer tools, or similar)\n Track record of taking ownership of features or components end-to-end — from design through production — within a larger platform or system\n Comfortable operating in an ambiguous, fast-changing domain where tooling and best practices are still being defined\n Strong problem-solving skills and the ability to work independently or as part of a tight-knit, cross-functional team\n Excited to work directly with ML engineers and customer-facing teams, including challenging assumptions in designs and metrics when platform behavior, model behavior, and customer needs intersect\n Gives direct, substantive feedback on designs and code, and takes it the same way — and mentors others as they grow\n \n Nice to have:\n \n Deep experience building or maintaining observability, monitoring, or evaluation systems for ML/LLM-powered products in production\n Familiarity with agent architectures — tool use, planning, multi-agent orchestration\n Exposure to MLOps, feature stores, model serving, or experiment infrastructure\n Experience working in regulated or enterprise contexts\n Experience reviewing others’ technical designs or mentoring engineers at a senior/staff level\n Compensation packages at Scale for eligible roles include base s","salary_min":216000,"salary_max":270000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["data-pipeline","mlops","generative-ai","agents","autonomous-vehicles","llm","distributed-systems"],"apply_url":"https://job-boards.greenhouse.io/scaleai/jobs/4714509005","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-14T20:12:46Z","expires_at":"2026-08-14T14:01:47.306812Z","created_at":"2026-07-15T14:01:47.543291Z","updated_at":"2026-07-15T14:01:47.543291Z","company_name":"Scale AI","company_slug":"scale-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=scale.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/96c4b57f-c214-4de0-829c-cda4957c7a17"},{"id":"1a206bd4-e5b5-4a4d-8384-65e3e9c3f4ec","company_id":"2721f049-2cf2-4e3e-82d0-8d8df89c8f90","title":"SDR, Tavily","slug":"sdr-tavily-497ebf81","description":"About Nebius: \n Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure.\n Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI.\n Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R\u0026D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R\u0026D.\n \n About Tavily \n We’re building the search engine for AI agents. Our API is designed from the ground up to power RAG and real-time reasoning in AI systems. By connecting LLMs to high quality, trustworthy web content, we help developers build agents that are not only intelligent, but also informed.\n We work with some of the most innovative teams in AI, from small startups shaping the ecosystem to the largest enterprises deploying AI at scale. Whether it’s powering sales assistants, research copilots, or internal knowledge tools, we’re the missing link between LLMs and the real world \n The role \n We’re hiring to expand on the immediate success and impact our founding SDR team has had. You will be the engine behind the engine, helping convert high-volume developer inbound into qualified opportunities and building an outbound motion to the teams creating cutting edge agents and AI products.\n Your responsibilities will include:   \n \n Promptly follow up with inbound and outbound prospects via email, LinkedIn, and calls to ensure no lead slips through the cracks.\n Build a deep understanding of Tavily’s ICP: AI engineers, data science teams, and product leaders building agentic systems to identify where they need grounded, real-time search in their product\n Qualify opportunities and book meetings for the Sales team, ensuring they are equiped with the correct information to win the deal.\n Provide structured feedback on signals, workflows, and outputs to help us improve Tavily based on real-life testing.\n \n We expect you to have:   \n \n 1–2 years of Sales, SDR, Analytics or Computer Science experience in a SaaS or tech environment preferred (open to exceptional entry-level candidates).\n You are in Austin and excited about an in-person office environment (think 4 days per week).\n You're resilient, energized by building relationships, and genuinely excited to learn.\n You’re genuinely curious about AI and enjoy learning what new products and teams are building, even if you’re not technical yourself.\n Ability to balance high-volume outreach with thoughtful experimentation and feedback.\n \n Key employee benefits in the US: \n \n Health insurance:  100% company-paid medical, dental, and vision coverage for employees and families.\n 401(k) plan:  Up to 4% company match with immediate vesting.\n Parental leave:  20 weeks paid for primary caregivers, 12 weeks for secondary caregivers.\n Remote work reimbursement:  Up to $85/month for mobile and internet.\n Disability \u0026 life insurance : Company-paid short-term, long-term and life insurance coverage.\n \n \n Pay Transparency \n We offer competitive compensation and benefits packages. Actual compensation will be determined based on job-related factors, including experience, skills, qualifications, the level at which the candidate is hired, and geographic location, consistent with applicable law.\n Base Compensation Range\n $71,700 — $89,600 USD \n Benefits \u0026 Perks: \n \n Competitive compensation\n Career growth and learning opportunities\n Flexibility and ownership\n Collaborative and innovative culture\n Opportunity to work on impactful AI projects\n International environment and talented teams\n \n What's it like to work at Nebius: \n Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI \n Equal Opportunity Statement: \n Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law.\n Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. \n If you need accommodations during the application process, please let us know.","salary_min":71700,"salary_max":89600,"location":"Austin, TX","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["search","cloud","code-generation","rag","llm","agents"],"apply_url":"https://careers.nebius.com/?gh_jid=4927819101","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-14T20:05:03Z","expires_at":"2026-08-14T14:17:14.484341Z","created_at":"2026-07-15T14:17:14.584259Z","updated_at":"2026-07-15T14:17:14.584259Z","company_name":"Nebius","company_slug":"nebius","company_logo_url":"https://www.google.com/s2/favicons?domain=nebius.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/1a206bd4-e5b5-4a4d-8384-65e3e9c3f4ec"},{"id":"0003f63a-b2b2-44e0-b588-7a3de39a2516","company_id":"28040a6c-6f94-41a4-b15a-f2e4520188ff","title":"Agent Experience Designer, Agentic Voice","slug":"agent-experience-designer-agentic-voice-00a4cb3f","description":"About Dialpad Dialpad is the AI-native business communications platform. We unify calling, messaging, meetings, and contact center on a single platform - powered by AI that understands every conversation in real time. \n More than 70,000 companies around the globe, including WeWork, Asana, NASDAQ, AAA Insurance, COMPASS Realty, Uber, Randstad, and Tractor Supply, rely on Dialpad to build stronger customer connections using real-time, AI-driven insights. \n We’re now leading the shift to Agentic AI: intelligent agents that don’t just analyze conversations but take action by automating workflows, resolving customer issues, and accelerating revenue in real time. Our DAART initiative (Dialpad Agentic AI in Real Time) is redefining what a communications platform can do. \n Visit dialpad.com to learn more. \n Being a Dialer At Dialpad, AI isn’t just a feature; it’s how our teams do their best work every day. We put powerful AI tools in every employee’s hands so they can move faster, think bigger, and achieve more. \n We believe every conversation matters. And we’ve built the platform that turns those conversations into insight and action, for our customers and ourselves. \n We look for people who are intensely curious and hold themselves to a high bar. Our ambition is significant, and achieving it requires a team that operates at the highest level. We seek individuals who embody our core traits: Scrappy, Curious, Optimistic, Persistent, and Empathetic . \n Your role As an Agent Experience Designer — Agentic Voice, you’ll own the voices, personalities, and interactions that make an AI agent feel intuitive, empathetic, and human. We are going all-in on agentic AI under one core idea: stop answering, start resolving. A voice agent that resolves is only as good as the experience it delivers, and designing that entire voice experience is your job. \n Reporting directly to the VP of AI Products, you’ll collaborate hand-in-hand with our AI engineers to shape model judgment through prompts and flow orchestration, rather than hard-coded branches. You’ll also help create a centralized persona system, voice standards, and the universal quality bar that forward-deployed VX designers will apply account-by-account in the field. \n In addition, you’ll help bring a deep sense of behavioral and emotional design to our platform, ensuring our agents have the taste, pacing, and vocabulary to sound truly competent and empathetic across both happy paths and high-stakes moments. \n This position has the opportunity to be based in our San Ramon, US office.\n What you’ll do \n \n Own the agent's global voice, character, and personality, maintaining personal consistency across every vertical we ship. \n Own the standard handoff patterns and design systems, ensuring seamless transitions where context is fully preserved when an agent passes a caller to a human. \n Own the universal platform quality bar, defining and measuring Consistency, Fluency, and Latency (CFL) and tying personal decisions directly to core metrics like resolution, containment, and sentiment. \n Make the voice palette and establish house standards for pacing, prosody, and emphasis that forward-deployed teams will use to build brand-specific experiences. \n Partner with AI engineers to orchestrate behavior, escalation instincts, confirmation patterns, and graceful recovery workflows using advanced prompting rather than rigid dialogue trees. \n Research and design for distinct behavioral and emotional user states, ensuring the agent adapts seamlessly whether interacting with a patient disputing a bill or a dispatcher tracing a late delivery. \n \n Skills you’ll bring \n \n Experience: 5+ years of dedicated experience shaping voice user interfaces (VUI), character writing, conversation design, or complex conversational/agentic systems. \n Bachelor's degree in Linguistics, Communication, Psychology, Design, or equivalent practical experience. \n Demonstrated experience shaping voice user interfaces (VUI), character writing, or complex conversational/agentic systems. \n Fluency with LLM-based agent behaviors, prompt engineering, and prompt orchestration (knowing how design choices alter model outputs without relying on code). \n Fluency with Text-to-Speech (TTS) controls, including voice selection, SSML tuning, pacing, and emphasis to set broad platform standards. \n An exceptional portfolio that highlights voice systems, written persona standards, and interactive logic rather than just static flow diagrams. \n Experience in regulated, high-stakes verticals (e.g., healthcare, financial services, legal) is a strong plus. \n Strong taste and an ear for dialogue—the ability to articulate a character on a page and translate it into consistent AI behavior under pressure. \n \n For exceptional talent based in California, the target base salary range for this position is posted below. Our salary ranges are determined by role, level, and location. The range displayed on each job posting","salary_min":147000,"salary_max":186000,"location":"San Ramon, US","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["agents","speech","llm","healthcare"],"apply_url":"https://job-boards.greenhouse.io/dialpad/jobs/8633475002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-14T20:04:06Z","expires_at":"2026-08-14T14:23:00.471126Z","created_at":"2026-07-15T14:23:00.567276Z","updated_at":"2026-07-15T14:23:00.567276Z","company_name":"Dialpad","company_slug":"dialpad","company_logo_url":"https://www.google.com/s2/favicons?domain=dialpad.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/0003f63a-b2b2-44e0-b588-7a3de39a2516"},{"id":"dbf3ed2d-61a8-46d4-8f99-13cd04d28f0e","company_id":"4c109027-78ec-41cd-b57a-dc58e47d0bd0","title":"Senior Machine Learning Scientist I, Model-Driven Optimization","slug":"senior-machine-learning-scientist-i-model-driven-optimization-eb80e6bd","description":"The Role:  \n Generate:Biomedicines is seeking a creative, rigorous, and execution-oriented machine learning scientist to join our Model-Driven Design team. This role will focus on building the ML methods, data strategies, and closed-loop systems that determine what we design, build, test, and learn from next.\n The Model-Driven Design team works at the interface of machine learning, protein design, engineering, and experimental science. We develop and apply models and quantitative frameworks that help Generate discover and optimize therapeutic proteins. In this role, you will help advance the technical foundation of our lab-in-the-loop protein optimization platform, with a focus on sequential decision-making, experimental design, property modeling, and scalable design systems.\n We are looking for someone who can serve as a technical leader and hands-on individual contributor, driving complex, high-impact work from problem framing through implementation, deployment, and experimental impact. The ideal candidate combines depth in probabilistic machine learning, Bayesian optimization, active learning, or related approaches with the practical judgment and engineering discipline to turn technical ideas into reliable systems that drive impact. You will partner closely with protein designers, wet-lab scientists, ML scientists, and engineers to build durable capabilities that accelerate therapeutic discovery.\n This role is part of a highly collaborative team environment that balances in-person collaboration with hybrid flexibility based out of our Somerville, MA office. \n Here's how you will contribute: \n \n Develop new machine learning methods and systems for lab-in-the-loop protein optimization, including property models and multi-objective optimization strategies for therapeutic protein design.\n Shape data-generation and data-use strategies that make experimental campaigns maximally informative for model improvement, therapeutic optimization, and future design cycles.\n Build and apply LLM-enabled and agentic workflows that help scientists explore design hypotheses, connect models to data and experiments, and accelerate iterative learning.\n Design, implement, test, and maintain production-quality ML models, software components, and data workflows, with attention to reliability, reproducibility, observability, and computational efficiency.\n Partner with ML engineering and software teams to integrate these components into robust, scalable platform capabilities, with clear ownership across team boundaries.\n Collaborate closely with protein designers and wet-lab scientists to ensure models and optimization systems are grounded in experimental reality and deliver measurable impact.\n Identify important technical gaps, develop proposals, define milestones, align stakeholders, and help set technical direction across cross-functional programs.\n Communicate clearly across disciplines and help raise technical standards across ML, engineering, protein design, and experimental teams.\n \n The Ideal Candidate will have: \n \n PhD in machine learning, computational biology, computer science, applied mathematics, engineering, or a related quantitative field.\n Strong practical experience with probabilistic machine learning, Bayesian optimization, active learning, experimental design, or related approaches for sequential decision-making under uncertainty.\n Experience developing machine learning methods or systems for biological, biomedical, or experimental scientific data, with an ability to reason about noisy assays, sparse labels, experimental bias, and data-generation strategy.\n Demonstrated ability to translate ML ideas into systems, tools, or workflows that affect real scientific, experimental, or product decisions.\n Strong Python skills and experience with modern ML frameworks such as PyTorch, JAX, or similar tools.\n Strong systems thinking and ability to design technical interfaces, reason about system tradeoffs, and partner with engineering teams to build scalable, maintainable ML infrastructure.\n Excellent communication skills and ability to bridge ML, engineering, protein design, and experimental stakeholders.\n Pragmatic, collaborative working style, with the ability to bring structure to open-ended problems and balance scientific rigor with execution in fast-moving, cross-functional environments.\n \n Nice to have \n \n Experience in protein design, protein engineering, antibody engineering, biologics discovery, or drug development.\n Experience partnering with experimental teams on design-build-test-learn cycles, high-throughput screening, directed evolution, pooled libraries, or model-guided experimental campaigns.\n Experience with multi-objective optimization, uncertainty calibration, model-guided library design, or experimental campaign planning.\n Experience developing and applying deep learning models, including transformer-based architectures\n Experience building or applying LLM agents, scientific copilots, or agentic syste","salary_min":192000,"salary_max":265000,"location":"Somerville, MA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["agents","llm","healthcare","code-generation","deep-learning","pytorch","machine-learning"],"apply_url":"https://generatebiomedicines.com/open-positions?gh_jid=4696856006","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-14T19:16:51Z","expires_at":"2026-08-14T14:15:56.335011Z","created_at":"2026-07-15T14:15:56.439634Z","updated_at":"2026-07-15T14:15:56.439634Z","company_name":"Generate Biomedicines","company_slug":"generate-biomedicines","company_logo_url":"https://www.google.com/s2/favicons?domain=generatebiomedicines.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/dbf3ed2d-61a8-46d4-8f99-13cd04d28f0e"},{"id":"f115ce97-f6c4-4c2d-9602-6a9e48528e12","company_id":"b6db41bc-ba14-4906-b2f7-a3ce9289a346","title":"Software Engineer, AI Platform","slug":"software-engineer-ai-platform-305af12e","description":"WHO WE ARE\n\nNotion is the collaborative AI workspace where teams and agents think together https://www.youtube.com/watch?v=vkpYpWfEK5s. We're building one place where your knowledge, projects, meetings, and AI tools live side by side, so work is faster, clearer, and less fragmented. Millions of individuals, small teams, and large companies run their work on Notion.\n\n\n\nNotinos (our employees) are customer zero in bringing this future of work to life. We care about craft, building things that last, and the belief that great work is still fundamentally human. Our goal isn’t to ship the next feature. Each and every team of Notinos is working to set the standard for how humans work together in the AI era. From building a business’s system of record to making and managing AI agents to automating away the busy work, we care deeply about giving our customers more time for their life’s work.\n\n\n\n\nABOUT THE ROLE:\n\nMillions of people use Notion — and this number is increasing every day. That means millions of people trust us to deliver a fast, reliable, and secure experience, and we value this more than anything. We want to keep earning trust, while also continuing to amaze our users with the tools they can build in Notion.\n\nThe AI Platform team is responsible for building the shared foundations that let Notion ship AI products quickly and operate them safely at scale. You’ll join a team of talented engineers focused on making speed and quality compatible: reliability and availability through provider changes, quality and correctness systems like evals and release gates, observability that makes failures explainable, and shared primitives for model integrations, context management, long-running actions, and cost/performance tradeoffs. Notion’s AI platform is vital to helping product teams move faster with production-grade guardrails as models, providers, and AI capabilities rapidly evolve.\n\nThis role can be based in either San Francisco or New York City. We work from our offices on Mondays, Tuesdays and Thursdays (our Anchor Days) because we do our best thinking and building together in person. We’re looking for someone who’s excited to work alongside the team during those days.\n\n\n\n\nWHAT YOU'LL ACHIEVE:\n\n - You'll own and play a pivotal role in the prototyping, development and scaling of systems and core AI platform primitives.\n\n - You’ll partner closely with product teams to provide paved paths and production-ready guardrails that help new AI features ship faster with less duplicated work.\n\n - You’ll work across infrastructure, shared libraries, APIs, and product integration points to make AI platform capabilities easy to adopt and high-leverage across Notion.\n\n - You’ll operate critical AI systems in production, using observability and diagnostics to understand provider/model behavior, debug failures, improve latency and cost, and evolve systems with minimal user disruption.\n\n - You’ll help Notion safely adopt new models, providers, and AI capabilities through versioning, controlled rollouts, compatibility layers, and clear quality/reliability gates.\n\n\n\n\nSKILLS YOU'LL NEED TO BRING:\n\n - Passion for AI systems at scale: You’ve worked on LLM, ML platform, data, or infrastructure teams that own critical shared systems. You understand the challenges of scaling reliability, latency, cost, and quality as usage and model complexity grow. You care deeply about building platforms that are dependable, efficient, and easy for other engineers to use.\n\n - Adaptable and curious: You like going deep on how systems behave in practice, especially when models, providers, and product requirements are changing quickly. You’re eager to use AI tools to work smarter and are willing to move across backend, infrastructure, libraries, and product code when that’s what the problem requires.\n\n - Extreme ownership: You’re comfortable working across ambiguous problem spaces, aligning stakeholders around a clear path forward, and driving execution with accountability. You take ownership of platform outcomes including reliability, quality, adoption, and operational follow-through beyond team boundaries.\n\n - Thoughtful problem-solving: For you, problem-solving starts with a clear and accurate understanding of the context. You can decompose ambiguous system behavior, debug across layers, and work toward clean, pragmatic solutions by yourself or with teammates. You’re comfortable asking for help when you get stuck.\n\n - Pragmatic and business-oriented: You understand that AI platform work is full of tradeoffs across quality, latency, cost, reliability, and speed of execution. You prioritize based on product and business impact, balancing craft with urgency and operational simplicity.\n\n\n\n\nNICE TO HAVES:\n\n - 2-4 years of experience as a Software Engineer\n\n - Experience with applied AI product development (prompting, evals, model integrations, or quality measurement).\n\n - You've built out and scaled data processing pipeli","salary_min":180000,"salary_max":201000,"location":"San Francisco, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"junior","tags":["agents","mlops","llm","data-pipeline","platform"],"apply_url":"https://jobs.ashbyhq.com/notion/a9d4a192-d31c-48d2-8156-e2a75d98eec1/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-14T14:23:31.706Z","expires_at":"2026-08-14T14:04:38.933673Z","created_at":"2026-07-15T14:04:39.064153Z","updated_at":"2026-07-15T14:04:39.064153Z","company_name":"Notion","company_slug":"notion","company_logo_url":"https://www.google.com/s2/favicons?domain=notion.so\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/f115ce97-f6c4-4c2d-9602-6a9e48528e12"},{"id":"02fdc710-8e20-40fd-aedd-05f740fa50ac","company_id":"377b9ca2-ac79-48a5-8657-da630f9e447d","title":"Senior Staff / Principal Machine Learning Scientist, AI Inference \u0026 Optimization","slug":"senior-staff-principal-machine-learning-scientist-ai-inference-optimization-8c8ecaa7","description":"About Netskope \n Today, there's more data and users outside the enterprise than inside, causing the network perimeter as we know it to dissolve. We realized a new perimeter was needed, one that is built in the cloud and follows and protects data wherever it goes, so we started Netskope to redefine Cloud, Network and Data Security. \n \n Since 2012, we have built the market-leading cloud security company and an award-winning culture powered by hundreds of employees spread across offices in Santa Clara, St. Louis, Bangalore, London, Paris, Melbourne, Taipei, and Tokyo. Our core values are openness, honesty, and transparency, and we purposely developed our open desk layouts and large meeting spaces to support and promote partnerships, collaboration, and teamwork. From catered lunches and office celebrations to employee recognition events and social professional groups such as the Awesome Women of Netskope (AWON), we strive to keep work fun, supportive and interactive.     Visit us at  Netskope Careers. Please follow us on LinkedIn and Twitter @Netskope . \n Positions are available at Senior Staff and above. Candidates are assessed individually and leveled according to their specific skills and background. \n About the role\n As a Senior Staff Machine Learning Scientist, you own the inference and optimization layer that makes AI in agentic workflows fast, efficient, and production-grade. You fine-tune and evaluate models, push latency and throughput on real hardware, and build the runtime that executes bounded AI tasks, validated against usage from Netskope’s large customer base so you optimize where the data points, not where you guess.\n What’s in it for you\n \n High-impact ownership. You own the model layer of a net-new product that changes the performance and economics of agentic AI.\n Cutting-edge, unusual stack. The hard, interesting inference problems live here: quantization, KV-cache and memory management, sparsity, fine-tuning, and hardware acceleration under real-world resource constraints.\n Real scale to build against. Netskope’s customer footprint gives you production signals most teams never see, so you deploy, validate, and iterate fast.\n \n What you will be doing\n \n Build and optimize the model inference path : quantization, KV-cache optimization, batching, and latency/memory/throughput tuning on constrained, commodity hardware.\n Fine-tune and evaluate models for bounded tasks; build eval harnesses that gate a capability to release on real accuracy, latency, and security relevance.\n Design and grow the task execution runtime (bounded sub-agents), pushing toward dynamic task generation and context compaction.\n Drive hardware acceleration / sparsity and support for larger models as the platform matures.\n Partner with the systems and backend engineers to ship capabilities end-to-end and iterate on real production signals.\n \n Required skills and experience\n \n 10+ years of overall industry experience , with 4+ years hands-on in ML/AI (model development, fine-tuning, and inference optimization).\n Hands-on with fine-tuning (e.g. LoRA/QLoRA), quantization (GGUF/AWQ/GPTQ), and inference runtimes (vLLM/SGLang, TensorRT-LLM, ONNX Runtime, llama.cpp, or MLX/CoreML). On-device or edge inference experience is a strong plus.\n Strong Python; comfort reaching into C++ for low-level interop is a plus.\n Solid grasp of transformer internals and the levers that move real inference performance and cost: KV cache, attention, batching, memory footprint.\n Fluency with agentic coding systems and genuine curiosity about agent harnesses like Claude Code, Pi, and Codex , so you should already be building with them, or itching to.\n Clear communication: able to distill a model or infra bottleneck into an actionable concept for cross-functional teammates.\n \n Education\n \n MS in Computer Science, Machine Learning, Electrical Engineering, or equivalent technical degree required, with a focus in AI/ML research; PhD in a related field strongly preferred.\n Compensation:  \n At Netskope, salary is one component of our competitive total rewards package. The salary range for this position is as listed below. This is a national range. For purposes of complying with applicable laws, the range applies to candidates in California, Colorado, Illinois, Maryland, New York, Washington, and other states. \n The successful candidate’s starting pay will also be determined based on job-related skills, experience, qualifications, location, and market conditions.  \n For all sales roles, the posted salary range is the On Target Earnings (OTE) range for the role, which is the sum of base salary and target commission amount at 100% goal achievement. \n In addition to salary, candidates may be eligible for other forms of compensation such as participation in a bonus plan (for non-sales roles) and a stock award program. Candidates may also be eligible for a comprehensive health plan and other benefits that can be reviewed at  Netskope Benefits site .","salary_min":182500,"salary_max":260500,"location":"San Jose, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["fine-tuning","agents","llm","cloud","machine-learning","inference"],"apply_url":"https://www.netskope.com/company/careers/open-positions/?gh_jid=8063869","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-14T04:20:32Z","expires_at":"2026-08-14T14:11:38.941823Z","created_at":"2026-07-15T14:11:39.076302Z","updated_at":"2026-07-15T14:11:39.076302Z","company_name":"Netskope","company_slug":"netskope","company_logo_url":"https://www.google.com/s2/favicons?domain=netskope.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/02fdc710-8e20-40fd-aedd-05f740fa50ac"},{"id":"c2d1990a-6a3b-4236-9209-26c9f4b3c2e0","company_id":"d3f1a010-47af-48d2-8b4e-a5953078daac","title":"Staff Product Manager, Infrastructure","slug":"staff-product-manager-infrastructure-d7875890","description":"WHY HARVEY\n\nAt Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come.\n\nThis is a rare chance to help build a generational company at a true inflection point. With 1500+ customers in 60+ countries, strong product-market fit, and world-class investor support, we’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched.\n\nOur team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you.\n\nAt Harvey, the future of professional services is being written today — and we’re just getting started.\n\n\n\n\nROLE OVERVIEW\n\nAs a Product Manager on the Core Platform team at Harvey, you'll own the strategy, roadmap, and execution for the infrastructure that powers every user interaction with our global legal AI platform. Harvey serves the world's leading legal teams, processing trillions of tokens and millions of daily requests, and your work will shape how that capability reaches our users.\n\nYou'll operate at the intersection of deep customer need and hard technical constraints, translating the workflows of lawyers and other professionals into product requirements that engineering can build against. You'll balance ambitious, zero-to-one product bets with the operational discipline required to keep a mission-critical platform reliable, scalable, and secure as we expand across products, regions, and customers. Your decisions will directly influence adoption, retention, and the trust that our enterprise customers place in Harvey.\n\n\n\n\nWHAT YOU'LL DO\n\nYou'll partner directly with our Head of Infrastructure to define and drive the product vision and roadmap for a core area of the Harvey platform, aligning it with company strategy and grounding it in evidence from customers (external and internal), data, and the market. You'll work closely with engineering, product, and go-to-market teams to ship high-quality products on a predictable cadence, and you'll own the outcomes those products produce.\n\nDay to day, you will own the entire infrastructure planning, prioritization, and roadmapping. You’ll make and communicate crisp prioritization decisions, balancing new capabilities against reliability, performance, and security. You'll define the metrics that matter for your area — adoption, engagement, quality, and business impact — and hold the team accountable to them. You'll also serve as the connective tissue across functions, ensuring that customer feedback, competitive dynamics, and technical realities all inform the product direction, and you'll raise the product bar across the organization through rigorous specs, reviews, and decision-making. Some projects include architecting multi-region deployment strategies, developing comprehensive observability infrastructure, and more.\n\n\n\n\n\n\n\nWHAT YOU HAVE\n\n - 6+ years of product management experience shipping and scaling software platforms in a production environment, with a track record of measurable impact\n\n - Experience owning complex, technical products end to end, including platform, infrastructure, or AI/ML capabilities\n\n - Strong ability to translate ambiguous problems and deep customer needs into clear strategy, crisp requirements, and prioritized roadmaps\n\n - Fluency working with engineering and design teams on technical trade-offs, and comfort engaging with concepts like distributed systems, APIs, and cloud infrastructure at a level sufficient to make informed decisions\n\n - Excellent analytical skills, with the ability to define metrics and use data to guide decisions\n\n - Outstanding written and verbal communication, and a demonstrated ability to influence and align stakeholders across functions\n\n - A high bar for quality, strong product judgment, and a \"spidey sense\" for where a product experience could break down\n\nNice to Have\n\n - Experience building products for legal, professional-services, or other expert users with demanding accuracy and trust requirements\n\n - Background with AI/ML products, LLM-powered applications, or high-throughput inference systems\n\n - Experience with multi-tenant, enterprise platforms subject to strict security and compliance requirements\n\n - Prior experience partnering closely with infrastructure or platform engineering teams\n\n - A prior career in law or another professional-services field, or e","salary_min":213600,"salary_max":300000,"location":"San Francisco, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"lead","tags":["distributed-systems","cloud","llm","agents","infrastructure"],"apply_url":"https://jobs.ashbyhq.com/harvey/d629fa64-599d-435c-b4ef-a925299ddac8/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-14T00:04:08.326Z","expires_at":"2026-08-14T14:02:50.722292Z","created_at":"2026-07-15T14:02:50.87055Z","updated_at":"2026-07-15T14:02:50.87055Z","company_name":"Harvey AI","company_slug":"harvey-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=harvey.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/c2d1990a-6a3b-4236-9209-26c9f4b3c2e0"},{"id":"390fffaf-6a9c-47f1-b56c-cd3a51ddec12","company_id":"d3f1a010-47af-48d2-8b4e-a5953078daac","title":"Senior Engineering Manager, Production Engineering","slug":"senior-engineering-manager-production-engineering-18d99048","description":"WHY HARVEY\n\nAt Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come.\n\nThis is a rare chance to help build a generational company at a true inflection point. With 1500+ customers in 60+ countries, strong product-market fit, and world-class investor support, we’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched.\n\nOur team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you.\n\nAt Harvey, the future of professional services is being written today — and we’re just getting started.\n\n\n\n\nROLE OVERVIEW\n\nHarvey is building the AI platform trusted by the world's leading law firms and enterprises. Our infrastructure is the foundation that powers every customer interaction, every model inference, and every production workload.\n\nWe're looking for a Senior Engineering Manager to lead our Infrastructure Foundation \u0026 Production Quality Engineering organization. This team is responsible for building and operating Harvey's core compute and networking infrastructure, Kubernetes platform, workflow orchestration platform, and production infrastructure foundations that enable engineering teams to move quickly with confidence.\n\nIn this role, you'll own the reliability, scalability, security, and efficiency of Harvey's infrastructure platform. You'll lead a team of high-performing engineers responsible for compute fleet management, capacity planning, infrastructure automation, and production operations. You'll partner closely with Product Engineering, Security, AI Infrastructure, and Platform teams to ensure our infrastructure scales with Harvey's rapid growth.\n\nYou'll report to the Head of Infrastructure and play a key leadership role in shaping the future of Harvey's infrastructure platform.\n\nAt Harvey, we value Decisiveness, Simplicity, and the belief that Job's Not Finished. We move quickly, prioritize clarity, and continuously raise the bar for engineering excellence.\n\n\n\n\nWHAT YOU'LL DO\n\n\nLEADERSHIP \u0026 STRATEGY\n\n - Lead, mentor, and grow a team of high-performing infrastructure engineers responsible for Harvey's production infrastructure foundation.\n\n - Foster a culture of operational excellence, engineering quality, customer ownership, and continuous improvement.\n\n - Partner with Engineering, Security, Product, and AI Infrastructure leaders to define long-term infrastructure strategy and execution priorities.\n\n - Drive technical direction for compute infrastructure, networking, Kubernetes, workflow orchestration, and production operations.\n\n - Lead cross-functional initiatives to improve reliability, scalability, security, operational efficiency, and infrastructure cost optimization.\n\n\nINFRASTRUCTURE FOUNDATION \u0026 PRODUCTION OPERATIONS\n\n - Own and operate Harvey's global compute and network infrastructure, ensuring high availability, scalability, reliability, and performance.\n\n - Manage compute resources to maximize utilization, performance, and service availability while supporting rapidly growing AI workloads.\n\n - Lead capacity planning, demand forecasting, and fleet lifecycle management to ensure infrastructure scales efficiently with business growth.\n\n - Operate and continuously improve Harvey's Kubernetes platform, including cluster provisioning, upgrades, monitoring, reliability, performance, and operational automation.\n\n - Own Harvey's Temporal-based workflow orchestration platform, ensuring reliable, scalable, and observable execution of distributed application workflows.\n\n - Drive infrastructure cost optimization through capacity management, resource rightsizing, workload efficiency improvements, and utilization monitoring.\n\n - Build and maintain secure infrastructure foundations, including identity and access management, network isolation, secrets management, auditing, and compliance controls.\n\n - Develop scalable Infrastructure-as-Code and automation frameworks using technologies such as Terraform and Pulumi.\n\n - Establish comprehensive observability, monitoring, alerting, incident response, and operational readiness practices across the infrastructure platform.\n\n\n\n\nWHAT YOU HAVE\n\n - 7+ years of software or infrastructure engineering experience, including 5+ years leading engineering teams.\n\n - Deep expertise operating large-scale cloud infrastructure on","salary_min":272000,"salary_max":355000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["distributed-systems","llm","cloud","agents"],"apply_url":"https://jobs.ashbyhq.com/harvey/8e420b36-6711-49dd-8a64-f246270af7d3/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-13T23:51:16.213Z","expires_at":"2026-08-14T14:02:47.991444Z","created_at":"2026-07-15T14:02:48.123991Z","updated_at":"2026-07-15T14:02:48.123991Z","company_name":"Harvey AI","company_slug":"harvey-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=harvey.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/390fffaf-6a9c-47f1-b56c-cd3a51ddec12"},{"id":"7befba03-6985-475e-9441-9bd1ccb173d8","company_id":"a0000000-0000-0000-0000-000000000001","title":"Research Engineer, Chip Design RL (Reinforcement Learning)","slug":"research-engineer-chip-design-rl-reinforcement-learning-39e9d4d0","description":"About Anthropic \n Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n About the RL teams \n Our Reinforcement Learning teams lead Anthropic's reinforcement learning research and development, playing a critical role in advancing our AI systems. We've contributed to all Claude models, with significant impacts on the autonomy and coding capabilities of Claude Fable 5 and Opus 4.8. Our work spans several key areas:\n \n Developing systems that enable models to use computers effectively\n Advancing code generation through reinforcement learning\n Pioneering fundamental RL research for large language models\n Building scalable RL infrastructure and training methodologies\n Enhancing model reasoning capabilities\n \n We collaborate closely with Anthropic's alignment and frontier red teams to ensure our systems are both capable and safe. We partner with the applied production training team to bring research innovations into deployed models, and are dedicated to implement our research at scale. Our Reinforcement Learning teams sit at the intersection of cutting-edge research and engineering excellence, with a deep commitment to building high-quality, scalable systems that push the boundaries of what AI can accomplish.\n About the role \n We're hiring for the Code RL team within the RL organization. As a Research Engineer, you'll advance our models' ability to design silicon. Hardware design is difficult and unforgiving – exactly the sort of domain we want Claude to excel at.\n You'll leverage your chip design expertise and turn it into tasks and signals for models to learn from. Specifically, you will: \n \n Invent, design, and implement RL environments and evaluations for agentic RTL generation, design (including formal) verification, physical design optimization.\n Work on cross-cutting RL considerations such as EDA-tool latency optimization and proxy rewards.\n Conduct experiments and shape our roadmap.\n Deliver your work into research and production training runs.\n Collaborate with other researchers and engineers across and outside Anthropic.\n \n You may be a good fit if you: \n \n Have expertise in ASIC or FPGA design: RTL, design verification (UVM, formal methods, coverage-driven), physical design (synthesis, place-and-route, timing closure), PPA optimization, DFT, ECOs.\n Are fluent with industry EDA tools and processes.\n Have taped out chips and have experience going from spec to silicon.\n Know how to balance research exploration with engineering implementation.\n Are passionate about AI's potential and committed to developing safe and beneficial systems.\n \n Strong candidates may also have: \n \n Experience with reinforcement learning, evaluations or environments.\n Built tooling or automation around chip design flows.\n Worked on ML accelerators or high-performance compute hardware.\n Familiarity with high-level synthesis or architecture simulators.\n The annual compensation range for this role is listed below. \n For sales roles, the range provided is the role’s On Target Earnings (\"OTE\") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\n Annual Salary:\n $500,000 — $850,000 USD \n Logistics \n Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\n Required field of study:  A field relevant to the role as demonstrated through coursework, training, or professional experience\n Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position\n Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.\n Visa sponsorship:  We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.\n We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To","salary_min":500000,"salary_max":850000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["code-generation","reinforcement-learning","fine-tuning","search","llm","alignment","agents","research"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5231612008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-13T22:19:12Z","expires_at":"2026-08-14T14:00:27.276583Z","created_at":"2026-07-15T14:00:27.407964Z","updated_at":"2026-07-15T14:00:27.407964Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/7befba03-6985-475e-9441-9bd1ccb173d8"},{"id":"536847ab-380b-4023-a67d-e6f42968d89e","company_id":"ccb23d77-c69e-462b-a941-02ce99527e78","title":"Senior Software Engineer (Data Engineering and Infrastructure)","slug":"senior-software-engineer-data-engineering-and-infrastructure-fbb63209","description":"Who we are \n Aurora’s mission is to deliver the benefits of self-driving technology safely, quickly, and broadly.\n The Aurora Driver  will create a new era in mobility and logistics, one that will bring a safer, more efficient, and more accessible future to everyone.\n  \n At Aurora, you will tackle massively complex problems alongside other passionate, intelligent individuals, growing as an expert while expanding your knowledge. For the latest news from Aurora, visit  aurora.tech  or follow us on  LinkedIn .\n  \n What we are looking for \n Aurora hires talented people with diverse backgrounds who are ready to help build a transportation ecosystem that will make our roads safer, get crucial goods where they need to go, and make mobility more efficient and accessible for all. We are seeking a talented and experienced Software Engineer to join our data engineering and infrastructure team. In this role, you will be a key contributor to the design, development, and maintenance of our data platform, building the scalable and reliable systems that enable our organization to leverage data for insights and product innovation. You will work on the core data lake and data warehouse ecosystem infrastructure, data pipelines, and tools that process data at massive scale, ensuring it is accessible, high-quality, and secure.\n In this role, you will \n \n Design, build, and maintain robust and scalable data pipelines and ETL/ELT processes to ingest, transform, and load data from various sources into our data warehouse.\n Develop and manage data infrastructure components using AWS cloud services and infrastructure-as-code tools like Terraform.\n Collaborate with data scientists, analysts, autonomy engineering teams and product teams to understand their data needs and build solutions that meet their requirements.\n Optimize data processing systems for performance, reliability, and cost-efficiency.\n Implement monitoring, alerting, and logging for data pipelines and infrastructure to ensure operational stability.\n Champion best practices in data governance, data quality, and security.\n Work closely with other senior team members and management to improve the data ecosystem toolings, refine user experience, and continuously polish team roadmap.\n \n Required Qualifications \n \n Master’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.\n 5+ years of professional experience in software engineering, with a focus on data-related projects.\n Proficiency in at least one programming language commonly used for data engineering (e.g., Python, Go or C++).\n Solid experience with big data processing frameworks like Presto/Trino, EMR, Apache Spark, Flink, Kinesis Data Stream, or similar technologies.\n Hands-on experience with cloud platforms (AWS, GCP, or Azure) and their data services (e.g., S3, Redshift, BigQuery, Glue).\n Strong knowledge of SQL and experience working with relational and NoSQL databases.\n Intermediate knowledge of data analytics infrastructure, including data transformation tools such as DBT and visualization frameworks and tools\n Experience with building and managing data pipelines using an orchestrator like Apache Airflow.\n Able to systematically approach open-ended questions to identify pragmatic data solutions that scale\n Able to work effectively in a highly cross-functional, fast-moving and high-stakes environment\n Proven ability to communicate technical, data-driven solutions to both technical and non-technical audiences across stakeholders\n \n Desirable Qualifications  \n \n Experience with AI toolings, LLM and agentic frameworks\n Experience with data warehousing solutions like Snowflake or data lake architectures.\n Familiarity with modern data stack tools and practices.\n A passion for building elegant, scalable, and maintainable systems.\n Experience using Amazon Web Services (AWS) tools\n \n The base salary range for this position is $146K-$234K per year.  Aurora’s pay ranges are determined by role, level, and location. Within the range, the successful candidate’s starting base pay will be determined based on factors including job-related skills, experience, qualifications, relevant education or training, and market conditions. These ranges may be modified in the future. The successful candidate will also be eligible for an annual bonus, equity compensation, and benefits. \n  #LI-SP1 \n Working at Aurora At Aurora, we bring together extraordinarily talented and experienced people united by the strength of our values. We operate with integrity, set outrageous goals, and build a culture where we win together — all without any jerks.\n We believe in-person work increases collaboration, empathy and our ability to lead effectively. As a result, we operate in a hybrid work environment where Aurorans are in office at least 3 days per week.\n Our Careers page provides insight into what it is like to work at Aurora, and you can find all the latest updates in our Newsroom .\n Our c","salary_min":146000,"salary_max":234000,"location":"Pittsburgh, PA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["cloud","agents","autonomous-vehicles","llm","data-pipeline","infrastructure","data-science","data-engineering"],"apply_url":"https://aurora.tech/jobs/8628064002?gh_jid=8628064002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-13T13:23:27Z","expires_at":"2026-08-14T14:06:40.546103Z","created_at":"2026-07-15T14:06:40.677939Z","updated_at":"2026-07-15T14:06:40.677939Z","company_name":"Aurora Innovation","company_slug":"aurora-innovation","company_logo_url":"https://www.google.com/s2/favicons?domain=aurora.tech\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/536847ab-380b-4023-a67d-e6f42968d89e"},{"id":"f5536ef4-fbd2-4708-bd41-546593698786","company_id":"52f44519-9f93-4eac-ae0b-8be13e385ebe","title":"Research Engineer","slug":"research-engineer-evals-22a46522","description":"RESEARCH ENGINEER\n\n\n\nYou'll build the evaluation systems that tell us whether Firecrawl actually works. That sounds simple. It isn't. Our core promise, convert any URL into clean, structured, LLM-ready data reliably, is hard to measure rigorously across millions of different websites, formats, and edge cases. As the systems we're measuring get more complex, the question \"did that work?\" gets harder, not easier.\n\nThis isn't an eval role where you inherit a framework and run benchmarks. You'll design the metrics, build the pipelines, generate the datasets, and own the feedback loop from output quality back to model and product decisions. If you care about what \"good\" actually means and have the engineering depth to measure it, this is the role.\n\n\n\nSalary Range: $210,000–$275,000/year (Range shown is for U.S.-based employees in San Francisco, CA. Compensation outside the U.S. is adjusted fairly based on your country's cost of living.)\n\nEquity Range: Competitive equity — details shared during the process.\n\nLocation: San Francisco, CA (Hybrid, on-site required)\n\nJob Type: Full-Time\n\nExperience: 4+ years in ML, research engineering, or data-heavy backend, with real evaluation work\n\nVisa: Must already be authorized to work in the US or our eligible remote-hire regions. We're not able to sponsor visas right now, though that may change down the line.\n\n\n\n\nABOUT FIRECRAWL\n\nFirecrawl is the easiest way to turn the web into data AI agents can use. One API call converts any URL into clean, LLM-ready markdown or structured data - the boring-hard problem everyone building with LLMs eventually hits, solved.\n\nWe hit 8 figures in ARR in year one and more than doubled it in year two. We have 147k+ GitHub stars, and developers, agents, and category-defining AI companies build on us every day. Growth like this is rare, and we're just getting started.\n\nWe're a small team punching far above our weight. Everyone here owns a real piece of the product and company, end to end, and runs it themselves - no hiding behind process or headcount.\n\nThis is a place for people who want to work at the frontier: an AI company building the infrastructure other AI companies run on, not one bolting AI onto an existing product. We move fast, go deep, and are building the tools superintelligence will rely on to gather data from the web.\n\n\n\n\nWHAT YOU'LL DO\n\n - Design the metrics that define what \"good output\" actually means across millions of sites, formats, and edge cases\n\n - Build the pipelines and harnesses that measure quality rigorously and at scale\n\n - Generate and curate the datasets that make evaluation trustworthy\n\n - Own the feedback loop from output quality back to model and product decisions\n\n - Turn \"did that work?\" into an answer the whole team can act on\n\n\n\n\nWHAT WE'RE LOOKING FOR\n\n - You have the engineering depth to build real evaluation systems, not just run existing ones\n\n - You care deeply about what \"good\" means and how to measure it rigorously\n\n - You're comfortable owning ambiguous problems where the metric itself has to be invented\n\n - You move fast and close the loop - you'd rather ship, measure, and iterate than perfect on paper\n\n\n\n\nWHAT WE'RE NOT LOOKING FOR\n\n - Someone who only wants to run benchmarks someone else designed\n\n - A pure researcher who won't build the systems, or a pure engineer who won't think about methodology\n\n - Someone who needs a fully-specced ticket to start\n\n\n\n\nA NOTE ON PACE\n\nWe operate at an absurd level of urgency because the window for what we're building won't stay open forever. If that excites you, keep reading. If it doesn't, no hard feelings — but this role probably isn't for you.\n\n\n\n\nBENEFITS \u0026 PERKS\n\n\n\n\nAVAILABLE TO ALL EMPLOYEES\n\n - Salary that makes sense — $210,000-$275,000/year (U.S.-based), based on impact, not tenure\n\n - Own a piece — Gain competitive equity in what you're helping build\n\n - Generous PTO — 15 days mandatory, anything after 24 days, just ask (holidays excluded); take the time you need to recharge\n\n - Parental leave — 12 weeks fully paid, for all parents\n\n - Wellness stipend — $100/month for the gym, therapy, massages, or whatever keeps you human\n\n - Learning \u0026 Development — Expense up to $1,000/year toward anything that helps you grow professionally\n\n - Team offsites — A change of scenery, minus the trust falls\n\n - Sabbatical — 3 paid months off after 4 years, do something fun and new\n\n\n\n\nAVAILABLE TO US-BASED FULL-TIME EMPLOYEES\n\n - Full coverage, no red tape — Medical, dental, and vision (100% for employees, 50% for spouse/kids) — no weird loopholes, just care that works\n\n - Life \u0026 Disability insurance — Employer-paid short-term disability, long-term disability, and life insurance — coverage for life's curveballs\n\n - Supplemental options — Optional accident, critical illness, hospital indemnity, and voluntary life insurance for extra peace of mind\n\n - Doctegrity telehealth — Talk to a doctor from your couch\n\n - 401(k) plan — Retirement ","salary_min":210000,"salary_max":275000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["search","llm","agents","research"],"apply_url":"https://jobs.ashbyhq.com/firecrawl/25092c0e-9a32-4191-af79-050738213704/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-12T23:24:01.87Z","expires_at":"2026-08-14T14:17:49.764931Z","created_at":"2026-05-14T14:16:13.169544Z","updated_at":"2026-07-15T14:17:49.879632Z","company_name":"Firecrawl","company_slug":"firecrawl","company_logo_url":"https://www.google.com/s2/favicons?domain=firecrawl.dev\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/f5536ef4-fbd2-4708-bd41-546593698786"},{"id":"e02f64f7-97ca-4df9-8c15-82b1bb16fb2f","company_id":"52f44519-9f93-4eac-ae0b-8be13e385ebe","title":"Backend Infrastructure Engineer","slug":"backend-infrastructure-engineer-03331b6c","description":"BACKEND INFRASTRUCTURE ENGINEER\n\n\n\nYou'll work on the infrastructure that makes Firecrawl fast and reliable against a web that constantly changes and fights back. This is deep systems work: keeping success rates high against sites with aggressive defenses, managing the proxy layer that powers reliable data collection at scale, and pushing throughput and reliability under heavy, spiky load. When Firecrawl \"just works\" on a site that tries hard to stop it, that's this team. You'll own real infrastructure from day one, not tickets in a backlog.\n\n\n\nSalary Range: $200,000–$250,000/year (Range shown is for U.S.-based employees in San Francisco, CA. Compensation outside the U.S. is adjusted fairly based on your country's cost of living.)\n\nEquity Range: Competitive equity — details shared during the process.\n\nLocation: San Francisco, CA (Hybrid, on-site required)\n\nJob Type: Full-Time\n\nExperience: 3+ years in backend, networking, or infrastructure engineering\n\nVisa: Must already be authorized to work in the US or our eligible remote-hire regions. We're not able to sponsor visas right now, though that may change down the line.\n\n\n\n\nABOUT FIRECRAWL\n\nFirecrawl is the easiest way to turn the web into data AI agents can use. One API call converts any URL into clean, LLM-ready markdown or structured data - the boring-hard problem everyone building with LLMs eventually hits, solved.\n\nWe hit 8 figures in ARR in year one and more than doubled it in year two. We have 147k+ GitHub stars, and developers, agents, and category-defining AI companies build on us every day. Growth like this is rare, and we're just getting started.\n\nWe're a small team punching far above our weight. Everyone here owns a real piece of the product and company, end to end, and runs it themselves - no hiding behind process or headcount.\n\nThis is a place for people who want to work at the frontier: an AI company building the infrastructure other AI companies run on, not one bolting AI onto an existing product. We move fast, go deep, and are building the tools superintelligence will rely on to gather data from the web.\n\n\n\n\nWHAT YOU'LL DO\n\n - Build and maintain the infrastructure that powers reliable data collection at scale\n\n - Improve success rates against sites with aggressive defenses\n\n - Own the health, rotation, and cost efficiency of the proxy layer\n\n - Push throughput and reliability under heavy, spiky, real-world load\n\n - Debug hard, intermittent failures across the stack and make them stay fixed\n\n\n\n\nWHAT WE'RE LOOKING FOR\n\n - You've worked on networking, proxies, or high-throughput backend systems\n\n - You're relentless about reliability and comfortable in messy, adversarial problem spaces\n\n - You can own a system end to end and keep it healthy in production\n\n - You move fast and close the loop - you'd rather ship, measure, and iterate than perfect on paper\n\n\n\n\nWHAT WE'RE NOT LOOKING FOR\n\n - Someone who wants clean, well-defined problems only\n\n - Someone who avoids on-call or production ownership\n\n - Someone who needs the environment to be stable to do their best work\n\n\n\n\nA NOTE ON PACE\n\nWe operate at an absurd level of urgency because the window for what we're building won't stay open forever. If that excites you, keep reading. If it doesn't, no hard feelings — but this role probably isn't for you.\n\n\n\n\nBENEFITS \u0026 PERKS\n\n\n\n\nAVAILABLE TO ALL EMPLOYEES\n\n - Salary that makes sense — $200,000–$250,000/year, based on impact, not tenure\n\n - Own a piece — Gain competitive equity in what you're helping build\n\n - Generous PTO — 15 days mandatory, anything after 24 days, just ask (holidays excluded); take the time you need to recharge\n\n - Parental leave — 12 weeks fully paid, for all parents\n\n - Wellness stipend — $100/month for the gym, therapy, massages, or whatever keeps you human\n\n - Learning \u0026 Development — Expense up to $1,000/year toward anything that helps you grow professionally\n\n - Team offsites — A change of scenery, minus the trust falls\n\n - Sabbatical — 3 paid months off after 4 years, do something fun and new\n\n\n\n\nAVAILABLE TO US-BASED FULL-TIME EMPLOYEES\n\n - Full coverage, no red tape — Medical, dental, and vision (100% for employees, 50% for spouse/kids) — no weird loopholes, just care that works\n\n - Life \u0026 Disability insurance — Employer-paid short-term disability, long-term disability, and life insurance — coverage for life's curveballs\n\n - Supplemental options — Optional accident, critical illness, hospital indemnity, and voluntary life insurance for extra peace of mind\n\n - Doctegrity telehealth — Talk to a doctor from your couch\n\n - 401(k) plan — Retirement might be a ways off, but future-you will thank you\n\n - Pre-tax benefits — Access to FSAs and commuter benefits (US-only) to help your wallet out a bit\n\n - Pet insurance — Because fur babies are family too\n\n\n\n\nAVAILABLE TO SF-BASED EMPLOYEES\n\n - SF HQ perks — Snacks, drinks, team lunches, intense ping pong, and peak startup energy\n\n - E-Bike tran","salary_min":200000,"salary_max":250000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["llm","agents","infrastructure","backend"],"apply_url":"https://jobs.ashbyhq.com/firecrawl/6840bbee-aae5-4846-97a2-ec2e764ee75b/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-12T22:41:44.402Z","expires_at":"2026-08-14T14:17:49.939042Z","created_at":"2026-07-15T14:17:50.121535Z","updated_at":"2026-07-15T14:17:50.121535Z","company_name":"Firecrawl","company_slug":"firecrawl","company_logo_url":"https://www.google.com/s2/favicons?domain=firecrawl.dev\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e02f64f7-97ca-4df9-8c15-82b1bb16fb2f"},{"id":"a817e307-049e-4a51-a9c1-ec5a47864c6e","company_id":"52f44519-9f93-4eac-ae0b-8be13e385ebe","title":"Search Engineer","slug":"search-engineer-fe5d3ce2","description":"SEARCH ENGINEER\n\n\n\nYou'll build the systems that let anyone turn the open web into a search index. Firecrawl's search product is one of our fastest-growing surfaces, and we need engineers who can make crawling, ranking, and retrieval fast, reliable, and cheap at scale. You'll own real infrastructure from day one — not tickets in a backlog.\n\n\n\nSalary Range: $190,000-$260,000/year (Range shown is for U.S.-based employees in San Francisco, CA. Compensation outside the U.S. is adjusted fairly based on your country's cost of living.)\n\nEquity Range: Competitive equity — details shared during the process.\n\nLocation: San Francisco, CA (Hybrid, on-site required)\n\nJob Type: Full-Time\n\nExperience: 3+ years building production backend or infra systems\n\nVisa: Must already be authorized to work in the US or our eligible remote-hire regions. We're not able to sponsor visas right now, though that may change down the line.\n\n\n\n\nABOUT FIRECRAWL\n\nFirecrawl is the easiest way to turn the web into data AI agents can use. One API call converts any URL into clean, LLM-ready markdown or structured data - the boring-hard problem everyone building with LLMs eventually hits, solved.\n\nWe hit 8 figures in ARR in year one and more than doubled it in year two. We have 147k+ GitHub stars, and developers, agents, and category-defining AI companies build on us every day. Growth like this is rare, and we're just getting started.\n\nWe're a small team punching far above our weight. Everyone here owns a real piece of the product and company, end to end, and runs it themselves - no hiding behind process or headcount.\n\nThis is a place for people who want to work at the frontier: an AI company building the infrastructure other AI companies run on, not one bolting AI onto an existing product. We move fast, go deep, and are building the tools superintelligence will rely on to gather data from the web.\n\n\n\n\nWHAT YOU'LL DO\n\n - Design and build the crawling, indexing, and retrieval systems behind Firecrawl Search\n\n - Push down latency and cost per query while search volume grows\n\n - Improve ranking quality and freshness for LLM-driven retrieval\n\n - Own services end to end — design, ship, monitor, and iterate in production\n\n - Work directly with the Head of Search and the rest of the search team on the roadmap\n\n\n\n\nWHAT WE'RE LOOKING FOR\n\n - You've built and operated backend or distributed systems at real scale\n\n - You care about latency, cost, and correctness in equal measure\n\n - You're comfortable owning ambiguous problems and turning them into shipped systems\n\n - You move fast and close the loop — you'd rather ship, measure, and iterate than perfect on paper\n\n\n\n\nWHAT WE'RE NOT LOOKING FOR\n\n - Someone who needs a fully-specced ticket to start\n\n - Someone who wants to specialize narrowly and hand off everything else\n\n - Someone who optimizes for process over shipping\n\n\n\n\nA NOTE ON PACE\n\nWe operate at an absurd level of urgency because the window for what we're building won't stay open forever. If that excites you, keep reading. If it doesn't, no hard feelings — but this role probably isn't for you.\n\n\n\n\nBENEFITS \u0026 PERKS\n\n\n\n\nAVAILABLE TO ALL EMPLOYEES\n\n - Salary that makes sense — $190,000–$260,000/year, based on impact, not tenure\n\n - Own a piece — Gain competitive equity in what you're helping build\n\n - Generous PTO — 15 days mandatory, anything after 24 days, just ask (holidays excluded); take the time you need to recharge\n\n - Parental leave — 12 weeks fully paid, for all parents\n\n - Wellness stipend — $100/month for the gym, therapy, massages, or whatever keeps you human\n\n - Learning \u0026 Development — Expense up to $1,000/year toward anything that helps you grow professionally\n\n - Team offsites — A change of scenery, minus the trust falls\n\n - Sabbatical — 3 paid months off after 4 years, do something fun and new\n\n\n\n\nAVAILABLE TO US-BASED FULL-TIME EMPLOYEES\n\n - Full coverage, no red tape — Medical, dental, and vision (100% for employees, 50% for spouse/kids) — no weird loopholes, just care that works\n\n - Life \u0026 Disability insurance — Employer-paid short-term disability, long-term disability, and life insurance — coverage for life's curveballs\n\n - Supplemental options — Optional accident, critical illness, hospital indemnity, and voluntary life insurance for extra peace of mind\n\n - Doctegrity telehealth — Talk to a doctor from your couch\n\n - 401(k) plan — Retirement might be a ways off, but future-you will thank you\n\n - Pre-tax benefits — Access to FSAs and commuter benefits (US-only) to help your wallet out a bit\n\n - Pet insurance — Because fur babies are family too\n\n\n\n\nAVAILABLE TO SF-BASED EMPLOYEES\n\n - SF HQ perks — Snacks, drinks, team lunches, intense ping pong, and peak startup energy\n\n - E-Bike transportation — A loaner electric bike to get you around the city, on us\n\n\n\n\nINTERVIEW PROCESS\n\nApplication Review — Send us your work and a quick note on why this excites you. Show us what you've built —","salary_min":190000,"salary_max":260000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["search","llm","distributed-systems","agents"],"apply_url":"https://jobs.ashbyhq.com/firecrawl/762b4426-b4aa-4377-96d3-51f40c59cbf7/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-12T21:39:54.338Z","expires_at":"2026-08-14T14:17:50.097989Z","created_at":"2026-07-15T14:17:50.19743Z","updated_at":"2026-07-15T14:17:50.19743Z","company_name":"Firecrawl","company_slug":"firecrawl","company_logo_url":"https://www.google.com/s2/favicons?domain=firecrawl.dev\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/a817e307-049e-4a51-a9c1-ec5a47864c6e"},{"id":"f85cfe22-626a-4ca6-a996-ecbec9f694e8","company_id":"0565e120-4260-434a-91f5-7009f7fcbbab","title":"Staff Software Engineer, AI Foundations (Agent Optimization)","slug":"staff-software-engineer-ai-foundations-agent-optimization-51cb0084","description":"About Us \n Temporal is an open source programming model that can simplify code, make applications more reliable, and help developers focus on the important things like delivering features faster. We are on a mission to be the reliable foundation of every developer’s toolbox, and are building the team that will make that happen.\n  \n Our values guide us —they are present in how we show up, make decisions, and work together to make an impact. We’re curious, driven, collaborative, genuine and humble.\n  \n Temporal is growing and we are looking for those who share our values, challenge 'standard' thinking, and want to influence our future. If you have a passion for improving the developer experience, building world-class open-source software and communities, and want to be a part of our amazing team, we'd love to hear from you!\n \n About Us \n Temporal is an open source programming model that can simplify code, make applications more reliable, and help developers focus on the important things like delivering features faster. We are on a mission to be the reliable foundation of every developer’s toolbox, and are building the team that will make that happen.\n Our values guide us—they are present in how we show up, make decisions, and work together to make an impact. We’re curious, driven, collaborative, genuine and humble.\n Temporal is growing and we are looking for those who share our values, challenge 'standard' thinking, and want to influence our future. If you have a passion for improving the developer experience, building world-class open-source software and communities, and want to be a part of our amazing team, we'd love to hear from you!\n Summary \n We have an opening to hire a Staff Software Engineer - Agent Optimization \n Temporal provides a reliable foundation powering AI leaders such as OpenAI, NVIDIA, Cursor, Lovable, Replit, and others. Its adoption is expanding to users spanning a broad range of AI applications ranging from agents to data pipelines and everything in between.\n The mission of the AI Foundations team is to accelerate Temporal adoption across the entire ecosystem. Our approach combines a deep understanding of use cases with rigorous application of computer systems and software design principles.\n In this role, you will lead our agent agent optimization efforts. You will design tools and mechanisms to help Temporal users build agents that are optimized for token spend and response time while maintaining result quality. Model routing is a first step, but represents the tip of the iceberg of techniques that we can apply. For example, multi-agent architecture, cache policy, and context management are all relevant. Candidates for this role should have direct experience with this problem domain.\n You will work closely with other AI Foundations team members, e.g., those who focus on agentic development, maintaining a set of agents skills that lift performance of Codex, Claude Code, and similar tools for developers of Temporal applications. Other team members build ecosystem integrations or develop policy and security systems.\n If you thrive on blending theory and practice, then this is the right team for you. We are an action-oriented group that loves to ship fast and solve customer problems. We also seek thorough technical grounding for our work and invest in systems and practices that foster long-term success.\n Most of Temporal’s work is open source—see for yourself here: https://github.com/temporalio [new window]\n What You Will Do \n \n Work as a software engineer\n Maintain and expand a deep understanding of agentic coding\n Design and build agentic coding systems that we can trust to deliver high-quality outputs\n Develop a deep understanding of AI application development patterns and techniques, including emerging approaches and architectures.\n Take end-to-end ownership of new features, working with other teams to deliver exceptional reliability and a great developer experience.\n Work with multiple programming languages: Python and TypeScript, Java, Go.\n Serve as a domain expert on AI design patterns, collaborating with field staff to provide best-practices and canonical examples.\n Work directly with our developer community to debug issues that need expert attention and get feedback on Temporal features and APIs.\n Write public technical documentation describing Temporal concepts and APIs.\n Go the extra mile to support a customer in need, on the rare occasion that our teams’ engineering expertise is needed.\n Travel to meet your coworkers for a week once or twice a year.\n Attend the occasional developer conference to talk about how great Temporal is (optional).\n \n What You Won’t Do \n \n Work as a Data Scientist, Data Analyst, Devops SWE, or SRE.\n Work in an office (unless you want to, but you’d be by yourself). Temporal is a fully-remote company.\n Commit code that’s poorly-tested or works “most of the time”. Temporal aspires to be “Reliable as Gravity”, and we expec","salary_min":224000,"salary_max":302400,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"lead","tags":["distributed-systems","generative-ai","agents","data-pipeline"],"apply_url":"https://job-boards.greenhouse.io/temporaltechnologies/jobs/5184712007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-10T23:09:00Z","expires_at":"2026-08-14T14:07:37.278847Z","created_at":"2026-07-12T14:05:18.355113Z","updated_at":"2026-07-15T14:07:37.407734Z","company_name":"Temporal","company_slug":"temporal","company_logo_url":"https://www.google.com/s2/favicons?domain=temporal.io\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/f85cfe22-626a-4ca6-a996-ecbec9f694e8"},{"id":"624a206a-77bf-4580-8cdd-be32f7688f73","company_id":"a0000000-0000-0000-0000-000000000001","title":"Red Team Engineer, Safeguards","slug":"red-team-engineer-safeguards-ce8b1599","description":"About Anthropic \n Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n About the role \n Anthropic's Safeguards team is seeking a Red Team Engineer to help ensure the safety of our deployed AI systems and products. In this role, you'll take an adversarial approach to uncover vulnerabilities across our product ecosystem before they can be exploited by malicious actors. Your work will span from technical infrastructure vulnerabilities on our products to emergent risks from advanced AI capabilities.\n While you'll bring best practices from traditional security approaches, the focus is on broader safety implications and novel abuse unique to advanced AI systems and associated products. You'll investigate the full spectrum of potential abuse — from coordinated account manipulation and payment fraud to novel exploitation of product features — and simulate sophisticated threat actors who chain multiple attack vectors to achieve their objectives.\n Key responsibilities \n \n Conduct comprehensive adversarial testing across Anthropic's product surfaces, developing creative attack scenarios that combine multiple exploitation techniques\n Research and implement novel testing approaches for emerging capabilities, including agent systems, tool use, and new interaction paradigms\n Design and execute \"full kill chain\" attacks that emulate real-world threat actors attempting to achieve specific malicious objectives\n Build and maintain systematic testing methodologies that evaluate every aspect of our systems\n Develop automated testing frameworks to enable continuous assessment at scale\n Collaborate with Product, Engineering, and Policy teams to translate findings into concrete improvements\n Help establish metrics for measuring detection effectiveness of novel abuse\n \n Minimum qualifications \n \n Experience in penetration testing, red teaming, or application security\n Experience in model jailbreaking and testing large-scale agentic workflows for non-obvious prompt injection vectors\n Strong technical skills in web application security, including hands-on expertise with security testing tools (e.g., Burp Suite, Metasploit, custom scripting frameworks)\n Experience building custom automation, including LLM-specific testing frameworks\n A track record of discovering novel attack vectors and chaining vulnerabilities in creative ways\n A public body of work such as CVEs, blog posts, or disclosed bug bounty reports\n Strong written and verbal communication skills, with the ability to explain technical concepts to varied audiences\n \n Preferred qualifications \n \n Experience with AI/ML security or adversarial machine learning\n Understanding of AI safety considerations beyond traditional security, including modern guardrails against jailbreaks\n Experience testing API security and rate-limiting systems\n Background in testing business logic vulnerabilities and authorization bypass techniques\n Background in anti-fraud, trust \u0026 safety, or abuse prevention systems\n Familiarity with distributed systems and infrastructure security\n Familiarity with abuse detection mechanisms and the ability to engineer novel bypasses\n Adaptability to understand and build engagements around emerging threats outside your direct area of expertise\n The annual compensation range for this role is listed below. \n For sales roles, the range provided is the role’s On Target Earnings (\"OTE\") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\n Annual Salary:\n $320,000 — $405,000 USD \n Logistics \n Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\n Required field of study:  A field relevant to the role as demonstrated through coursework, training, or professional experience\n Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position\n Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.\n Visa sponsorship:  We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.\n We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, s","salary_min":320000,"salary_max":405000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["distributed-systems","security","agents","llm","payments","alignment","rust"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5320469008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-10T22:50:56Z","expires_at":"2026-08-14T14:00:26.778559Z","created_at":"2026-07-12T14:00:24.449098Z","updated_at":"2026-07-15T14:00:26.90493Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/624a206a-77bf-4580-8cdd-be32f7688f73"},{"id":"da65e8fc-123b-47b4-a19f-f1b5fde0fc84","company_id":"77beb456-fc80-40a4-b773-f0b17d1ece4c","title":"AI Infrastructure Engineer","slug":"ai-infrastructure-engineer-aabaa04d","description":"ABOUT MESHY\n\nHeadquartered in Silicon Valley, Meshy is the leading 3D generative AI company on a mission to Unleash 3D Creativity by transforming the content creation pipeline. Meshy makes it effortless for both professional artists and hobbyists to create unique 3D assets—turning text and images into stunning 3D models in just minutes. What once took weeks and cost $1,000 now takes just 2 minutes and $1.\n\nOur world-class team of top experts in computer graphics, AI, and art includes alumni from MIT, Stanford, and Berkeley, as well as veterans from Nvidia and Microsoft. Our talent spans the globe, with team members distributed across North America, Asia, and Oceania, fostering a diverse and innovative multi-regional culture focused on solving global 3D challenges. Meshy is trusted by top developers, backed by premiere venture capital firms like Sequoia and GGV, and has successfully raised $52 Million in funding.\n\nMeshy is the market leader, recognized as the No.1 in popularity among 3D AI tools (according to 2024 A16Z Games) and No.1 in website traffic (according to SimilarWeb, with 3 Million monthly visits). The platform boasts over 5 Million users and has generated 40 Million models.\n\nFounder and CEO Yuanming (Ethan) Hu earned his Ph.D. in graphics and AI from MIT, where he developed the acclaimed Taichi GPU programming language (27K stars on GitHub, used by 300+ institutes). His work is highly influential, including an honorable mention for the SIGGRAPH 2022 Outstanding Doctoral Dissertation Award and over 2,700 research citations.\n\n\n\n\n\nABOUT THE ROLE\n\n - This role sits at the intersection of platform engineering, site reliability, and applied ML systems. The function owns the reliability, scalability, and operability of Meshy's AI model serving stack, along with core engineering infrastructure. The team operates a conventional production infrastructure (CI/CD, build systems, deployment, runtime environments) and develops a model-serving platform that connects the models developed by our Research Team to product-facing backend systems. The position is systems-heavy, production-oriented, and focused on turning experimental model artifacts into robust, observable, and cost-efficient services.\n\n\n\n\n\nJOB RESPONSIBILITIES\n\n - Responsible for the design, development, and optimization of core capabilities for the AI inference platform, including key modules such as inference services, task scheduling, service orchestration, elastic scaling, and release governance.\n\n - Participate in the development of CPU/GPU resource management systems to optimize stability, resource utilization, and cost efficiency in scenarios where online inference and training tasks are run on the same cluster.\n\n - Drive the unified management and scheduling of GPU resources, and explore the practical implementation of capabilities such as MIG, MPS, time-sharing, and virtualization in real-world business operations.\n\n - Continuously optimize the throughput, latency, and availability of the inference pipeline, refining engineering quality in complex inference pipelines, multi-model collaboration, and high-concurrency scenarios.\n\n - Focus on R\u0026D efficiency, resource and cost management, online stability, and disaster recovery architecture design to drive the company’s continuous evolution in performance, reliability, and maintainability.\n\n - Explore AI-native infrastructure and automated operations to make infrastructure smarter and more user-friendly, supporting the company’s rapid expansion during its startup phase.\n\n \n\n\nQUALIFICATIONS\n\n - Bachelor’s degree or higher; majors in Computer Science, Software Engineering, Artificial Intelligence, Telecommunications, or related fields are preferred.\n\n - 1 to 3 years of experience in backend development, infrastructure, cloud-native platforms, machine learning platforms, or AI platforms.\n\n - Proficiency in at least one of Go or Python, with solid software engineering skills and a strong commitment to code quality.\n\n - Understanding of fundamental principles in Linux, operating systems, computer networks, and distributed systems; ability to independently identify and resolve complex engineering issues.\n\n - Practical development experience with Kubernetes, Docker, microservices, or distributed systems, with a basic understanding of production system stability.\n\n - Real-world project experience in areas such as model inference, task orchestration, resource scheduling, and service stability—beyond mere conceptual understanding.\n\n - Self-motivated, curious, and a fast learner; willing to take on greater ownership and broader responsibilities in a startup environment, while continuously learning and quickly adopting new technologies.\n\n\nNICE TO HAVE\n\n - Experience with GPU inference platforms, Kubernetes schedulers, Device Plugins, or related platform development.\n\n - Familiarity with frameworks such as Ray and Ray Serve, or experience in developing and optimizing model serving, distributed in","salary_min":175000,"salary_max":300000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["generative-ai","agents","microservices","mlops","distributed-systems","infrastructure"],"apply_url":"https://jobs.ashbyhq.com/meshy/e82eca7a-4704-4af3-a84f-94c6fb5e1034/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-10T21:33:17.539Z","expires_at":"2026-08-14T14:12:17.728298Z","created_at":"2026-04-13T15:01:38.817296Z","updated_at":"2026-07-15T14:12:17.854855Z","company_name":"Meshy","company_slug":"meshy","company_logo_url":"https://www.google.com/s2/favicons?domain=meshy.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/da65e8fc-123b-47b4-a19f-f1b5fde0fc84"},{"id":"390fea98-a9ba-4487-890a-77d135398888","company_id":"19955a21-2cd6-41fd-a4a8-19b7a942ac16","title":"Lead Value Engineer - Life Sciences","slug":"lead-value-engineer-life-sciences-4c32b22b","description":"Celonis is the global leader in Process Intelligence and the pioneer of Process Mining technology. As one of the world’s fastest-growing enterprise SaaS companies, we are changemakers pushing the boundaries of what’s possible. We invest heavily in advanced AI capabilities—specifically our Process Intelligence Graph—to turn data insights into immediate business action. We believe there is a massive opportunity to unlock global productivity and sustainability by placing intelligence at the core of every business process. Join our mission to make processes work for people, companies, and the planet.\n \n Role Description \n As a Lead Value Engineer specializing in the Life Sciences, you are pushing the envelope in solving business-critical problems for the world's largest, most diversified life science organizations. You will be working intimately with this strategic client, understanding their uniquely complex objectives—spanning from logistics to the precision distribution of advanced products—and building Celonis solutions using the world’s leading Process Intelligence (PI) platform in combination with top AI and ML technology partners (e.g., Microsoft, OpenAI, Databricks)..\n With Celonis’ Process Intelligence (PI) platform, we feed operational context to AI so it understands the intricate realities of our customers’ supply chain networks and enables them to industrialize AI. This unlocks real ROI on AI deployments at scale, ensuring life-saving products reach patients faster and safer. There is no AI without PI. You will prototype these solutions, demonstrate their value to Chief Supply Chain Officers (CSCOs) and operational leaders, and ensure successful implementation, adoption, and value realization to increase the footprint of Celonis across the life sciences sector.\n Key Responsibilities \n \n \n AI Discovery \u0026 Solutioning: Understand the client's overarching AI strategy and the distinct supply chain challenges across both their MedTech portfolios (e.g., mitigating global raw material shortages, optimizing supply chains, managing inventories, or accelerating quality batch releases). As a Celonis product and life sciences domain expert, translate these complex, multi-tiered logistics requirements into innovative AI solutions that drive measurable impact..\n \n Pre- and Post-Sales Execution: Actively drive the full customer lifecycle. Lead technical discovery and capability demonstrations during the pre-sales cycle, and remain deeply involved post-sale to guide implementation, ensuring agreed value and adoption thresholds in the supply chain are successfully reached.\n \n Hackathons \u0026 Prototyping: Think out of the box, have a „can-do“ attitude, and don’t shy away from complex, fragmented supply chain networks. Leverage cutting-edge AI technologies to rapidly build creative prototypes in customer hackathons, solving critical pain points in planning, sourcing, manufacturing, and distribution.\n \n Agentic Process Transformation: Support our customers in achieving real ROI out of AI deployments at scale, enabling a fundamental shift from traditional, rule-based automation to the use of autonomous AI agents empowered by our Celonis Process Intelligence Platform (e.g., autonomous inventory rebalancing or intelligent shipment exception handling).\n \n Proof Projects: End-to-end execution of business-critical Proof-of-Value projects. This includes architecting and delivering secure, scalable LLM/agent systems with RAG, tools, and guardrails, while seamlessly integrating with enterprise ERPs (e.g., SAP), Quality Management Systems (QMS), and strict regulatory frameworks (FDA, EMA, GxP).\n \n Domain \u0026 Industry Leadership: Serve as the internal and external technical subject matter expert for the Life Sciences Supply Chain, scaling knowledge across the organization regarding pharmaceutical manufacturing and logistics processes.\n \n Requirements \n \n \n 8+ years of experience leading technical pre-sales and post-sales engagements specifically within Life Sciences, Pharmaceutical, or MedTech supply chains. This includes defining AI roadmaps, building compelling ROI/TCO business cases, and guiding technical implementations through to value realization.\n \n Deep understanding of supply chain business processes native to Life Sciences (such as Sales \u0026 Operations Planning (S\u0026OP), Procure-to-Pay, Track \u0026 Trace, Cold Chain Management, or Quality Control/Batch Release) with the ability to translate high-level business needs into specific AI use cases.\n \n Expertise in generative AI techniques like RAG, few-shot learning, prompt engineering, multi-agent orchestration, multimodal understanding, or fine-tuning used to build high-impact use cases (e.g., intelligent chatbots for supplier collaboration, automated extraction of data from complex customs or quality documents).\n \n Solid knowledge of Python and common ML libraries (such as LangChain, pandas, pydantic, sklearn, PyTorch) as well as data engineering tools and techn","salary_min":157000,"salary_max":184000,"location":"New York, NY","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["pytorch","generative-ai","fine-tuning","llm","agents","cloud"],"apply_url":"https://job-boards.greenhouse.io/celonis/jobs/7800529003?gh_jid=7800529003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-10T21:19:52Z","expires_at":"2026-08-14T14:10:27.060437Z","created_at":"2026-07-12T14:07:50.167193Z","updated_at":"2026-07-15T14:10:27.247076Z","company_name":"Celonis","company_slug":"celonis","company_logo_url":"https://www.google.com/s2/favicons?domain=www.celonis.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/390fea98-a9ba-4487-890a-77d135398888"}],"market_demand_pack":{"amount_cents":2900,"api_checkout_url":"https://aidevboard.com/api/v1/checkout?product_id=aidevboard_ai_skills_demand_pack","checkout_url":"https://aidevboard.com/market-demand-pack?qc=api-jobs-market-demand-pack\u0026utm_campaign=skills_demand_pack\u0026utm_medium=jobs_api\u0026utm_source=api","currency":"USD","description":"Full ranked public AI/ML demand CSV, source job URLs, and decision brief with market and offer angles.","fulfillment":"automatic_email_after_paid_checkout","human_checkout_url":"https://aidevboard.com/market-demand-pack?qc=api-jobs-market-demand-pack\u0026utm_campaign=skills_demand_pack\u0026utm_medium=jobs_api\u0026utm_source=api","name":"AI Market Demand Pack","next_step":"Open checkout_url for Stripe Checkout, or call api_checkout_url to get the non-charging checkout handoff payload.","price_usd":29,"product_id":"aidevboard_ai_skills_demand_pack","quote_url":"https://aidevboard.com/api/v1/quote?product_id=aidevboard_ai_skills_demand_pack"},"page":1,"per_page":20,"total":2794,"total_pages":140}