{"access":{"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","employer_pilot_url":"https://aidevboard.com/verified-interview-pilot","mode":"open","register_url":"https://aidevboard.com/api/v1/register"},"candidate_resume_action":{"application_authorized":false,"candidate_charge":0,"endpoint":"https://aidevboard.com/api/v1/candidate/resume-preview","job_id_json_path":"jobs[].id","method":"POST","preview_requires_identity":false,"required_body_fields":["job_id","evidence_bullets"],"requires_explicit_human_review":true,"saved_artifact_protocol":"mcp","saved_artifact_requires_verified_human":true,"saved_artifact_tool":"compile_job_specific_resume","search_requires_identity":false,"status":"available_after_candidate_selects_job","submission_performed":false,"uses_candidate_verified_evidence":true},"degraded":false,"estimated":false,"has_next":true,"jobs":[{"id":"e1618a18-e64a-423b-82d5-022f365f7715","company_id":"df455adb-5b5f-43b9-a3bf-eb48405d2b7b","title":"Software Engineer, Applied AI Research","slug":"software-engineer-applied-ai-research-229b4f6a","description":"About Hightouch\n Hightouch is an Agentic Marketing Platform powered by the industry-leading Composable CDP. With complete brand context, customer data, and performance history in one place, every marketer finally has the power to build and ship end-to-end campaigns themselves. Teams move faster, stay on brand, and get AI marketing that actually works.\n Founded in 2019 and headquartered in San Francisco, Hightouch enables marketing teams to analyze performance, brainstorm ideas, and generate creative at a speed and quality that wasn't previously possible.\n Named a Leader in the 2026 Gartner® Magic Quadrant™ for Customer Data Platforms, Hightouch is trusted by leading enterprises like Domino's, Spotify, Aritzia, Cars.com, Ramp, and PetSmart.\n At Hightouch, our mission is to help our customers leverage data and AI to grow their businesses. The team is ambitious, impact-driven, efficient — and we believe humility, kindness, and compassion are essential to our success. If you're energized by velocity, obsessed with raising the bar, and want to build alongside people who care deeply about each other and our customers, we'd love to meet you.\n About the Role \n We’re looking to add  applied AI research engineers  to the team. The ideal candidate will have strong probabilistic and quantitative thinking, the ability to be highly creative and experimental with LLM applications, and ground their work in potential customer and product applications.\n Our applied research team focuses on finding new and innovative techniques to improve the frontier of what is possible in agentic AI marketing applications, with a particular focus on image and video. We focus less on theory or writing research papers, and more on experimenting, prototyping, and exploring new methods for applying generative AI to help our customers grow their companies.\n Example workstreams include:\n \n Quickly iterating and developing proofs of concept (POCs) to explore the maximum potential of integrating AI into data and marketing workflows\n Design and develop evaluation and improvement systems that can make advancements autonomously\n Expanding the frontier of what is feasible with AI generated video (e.g. making generated humans maximally realistic)\n Experiment with different approaches for generating brand assets that stay aligned to a particular company’s look and feel\n \n We're seeking talented, intellectually curious, and motivated individuals interested in exploring the forefront of AI. While no prior experience with LLMs and AI is required, strong technical skills, particularly in backend architecture or probabilistic systems, and product intuition are essential for understanding and executing these projects from start to finish.\n This is a senior role, but we focus on impact and potential for growth more than years of experience. The salary range for this position is $180,000 - $400,000 USD per year, which is location independent in accordance with our remote-first policy.\n Interview Process\n Our interview process focuses on evaluating fit for the most important dimensions of the role: product sense and creativity with LLMs, ability to architect backend systems, and alignment with Hightouch’s values. Notably, we don’t do any programming interviews as we believe they are low signal to noise and aren’t a good evaluation mechanism.\n \n Recruiter Screen [30m]:  Introductory call with our recruiting team to get to know each other and see if the role could be a good mutual fit.\n System Design Screen [60m]:  Designing a data processing or machine learning feature end-to-end (depending on background).\n Hiring Manager Interview [30m]:  Chat with hiring manager about past experiences and future operating preferences to assess fit on company values and operating principles.\n Agent Building Systems Interview [75m]:  Work with the interviewer to architect an agentic system at a conceptual level. The problem will be at a pretty high level - and have both product and customer requirements as well as technical.\n \n \n E-Verify Statement \n Hightouch participates in E-Verify. After you join the team, we'll verify your eligibility to work in the U.S. by submitting information from your Form I-9 to the Social Security Administration and, if needed, the Department of Homeland Security. This process happens post-hire only — we never use E-Verify to pre-screen applicants. \n E-Verify Notice E-Verify Notice (Spanish) Right to Work Notice Right to Work Notice (Spanish)","salary_min":180000,"salary_max":400000,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"senior","tags":["llm","agents","generative-ai","search","research"],"apply_url":"https://job-boards.greenhouse.io/hightouch/jobs/6174215004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-28T21:18:55Z","expires_at":"2026-09-29T13:38:07.054305Z","created_at":"2026-08-29T13:38:40.813703Z","updated_at":"2026-08-30T13:38:07.189907Z","company_name":"Hightouch","company_slug":"hightouch","company_logo_url":"https://www.google.com/s2/favicons?domain=hightouch.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e1618a18-e64a-423b-82d5-022f365f7715"},{"id":"c0509020-b00e-47f4-9cd5-7680b67c5c2f","company_id":"219030bb-3e37-4376-be76-0ea3c447e4b2","title":"Research Quality Analyst","slug":"research-quality-analyst-97f71c69","description":"About AlphaSense:  \n The world’s most sophisticated companies rely on AlphaSense to remove uncertainty from decision-making. With market intelligence and search built on proven AI, AlphaSense delivers insights that matter from content you can trust. Our universe of public and private content includes equity research, company filings, event transcripts, expert calls, news, trade journals, and clients’ own research content. \n The acquisition of Tegus by AlphaSense in 2024 advances our shared mission to empower professionals to make smarter decisions through AI-driven market intelligence. Together, AlphaSense and Tegus will accelerate growth, innovation, and content expansion, with complementary product and content capabilities that enable users to unearth even more comprehensive insights from thousands of content sets. Our platform is trusted by over 6,000 enterprise customers, including a majority of the S\u0026P 500. Founded in 2011, AlphaSense is headquartered in New York City with more than 2,000 employees across the globe and offices in the U.S., U.K., Finland, India, Singapore, Canada, and Ireland. Come join us! \n About Expert Insights: \n Expert Insights, which spans AlphaSense’s Expert Transcript Library and 1x1 Call Services offerings,  delivers a new and transformative form of market intelligence content. Through transcripts covering thousands of companies, it captures the unfiltered views and insights of business operators in the trenches, interviewed by professional investors who drill into key questions on what’s truly important about a company at each moment in time. AlphaSense’s library of over 220,000 transcripts is the market’s largest, covering all sectors of the economy, with thousands more published each month. Expert Insights is quickly becoming a table-stakes solution for institutional investors to choose the right companies to invest in while gaining rapid adoption among all other consumers of market intelligence from sell-side research and banking, consultancies, and large corporations.\n About the Team: \n The Directed Content Team is an integral part of the Expert Insights group, generating thousands of calls each quarter on high-value, strategic content targets — ensuring that the AlphaSense Expert Transcript Library (ETL) delivers comprehensive coverage to our users. The Directed Content team is responsible for identifying, recruiting, and onboarding the best possible experts from around the world based on the targets and topics we are looking to generate content against — ensuring that the interviews with those experts are of the highest possible quality and relevance.\n About the Role:  \n As the Research Quality Analyst on the Directed Content (DC) team, you will provide quality control across a continuous, high-volume research pipeline. This role uses sampling across the project lifecycle to catch quality issues, identify patterns, and drive improvements to process and AI prompting that raise the overall quality and consistency of DC's research output. You will be a key player in ensuring that our research is accurate, consistent, and valuable to our clients. This position offers an opportunity to develop expertise in AI-powered research and to contribute to refining workflows that generate trackable, high-quality insights.\n What You’ll Do:  \n \n Sample and review research projects at multiple stages — prompt sheets, knowledge bases, and published expert transcripts — to catch quality issues both before and after publication.\n Identify patterns in quality driven by process gaps or issues.\n Test and help deploy process and prompt fixes that address those patterns, so quality review becomes faster and narrower in scope over time.\n Assist in refining and iterating on AI prompts.\n Help develop and maintain QA checklists and documentation, standardizing quality practices.\n Track and analyze quality metrics, reporting on trends, and recommending process improvements.\n Support testing of new AI-driven tools and workflows as they're introduced to Directed Content.\n \n Who You Are: \n \n You have 1–3 years of experience in a role focused on quality assurance, content review, copy editing, research, or data analysis.\n You possess an exceptional eye for detail and a commitment to producing high-quality work.\n You are highly analytical and intellectually curious, with the ability to quickly understand new concepts and identify inconsistencies in data and text.\n You have excellent written and verbal communication skills, with experience editing and proofreading professional documents.\n You are comfortable working collaboratively in a fast-paced, cross-functional environment.\n You have a strong interest in technology and artificial intelligence; experience with AI or large language models is a significant plus.\n For base compensation, we set standard ranges for all roles based on function and level benchmarked against similar stage growth companies and internal comparables. In ","salary_min":62000,"salary_max":77000,"location":"Chicago, IL","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["payments","llm","data-pipeline","search","research"],"apply_url":"https://job-boards.greenhouse.io/alphasense/jobs/8692344002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-28T20:38:01Z","expires_at":"2026-09-29T13:40:52.736324Z","created_at":"2026-08-29T13:41:39.658903Z","updated_at":"2026-08-30T13:40:52.870924Z","company_name":"AlphaSense","company_slug":"alphasense","company_logo_url":"https://www.google.com/s2/favicons?domain=alpha-sense.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/c0509020-b00e-47f4-9cd5-7680b67c5c2f"},{"id":"49815b01-da50-4ed6-905c-a071c44a3cab","company_id":"a0000000-0000-0000-0000-000000000009","title":"Engineering Manager, FDE Infrastructure (NORAM)","slug":"engineering-manager-fde-infrastructure-noram-5b3d8276","description":"Who are we?\n\nCohere is the leading security-first enterprise AI company.  We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems.\n\nWe’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that.\n\nWe obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft.\n\nWe are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us!\n\nWe're looking for an Engineering Manager to lead our Deployment Engineering team. This isn't a typical management role — we need someone who leads from the front, gets their hands dirty, and drives impact. You'll manage a team of Forward Deployed Engineers who are on the front lines of deploying Cohere's North platform into customer environments. You should be ready to be a force to be reckoned with.\n\n\n\nLocation: North America (remote-first)\n\n\n\n\nWHAT YOU'LL DO\n\n - Lead and mentor a team of Forward Deployed Engineers \n\n - Drive end-to-end deployment of North in private cloud and on-premises environments\n\n - Take ownership of customer success from technical implementation through delivery\n\n - Collaborate closely with Product, Engineering, and Sales to shape how we deliver AI to enterprises\n\n - Mentor your team on cloud infrastructure, Kubernetes, and enterprise-grade deployments\n\n - Optimize performance for OpenSearch, databases, and other K8s services\n\n - Define scaling guidelines for GPU and CPU compute resources\n\n - Build processes \u0026 technology that scales — we're growing fast.\n\n\n\n\nWHAT WE'RE LOOKING FOR\n\n - 5+ years of experience in software engineering with demonstrated leadership\n\n - Hands-on experience deploying enterprise software at scale\n\n - Strong expertise in cloud infrastructure (Azure, AWS, GCP)\n\n - Experience with Kubernetes, Helm, and CI/CD pipelines\n\n - High agency — you don't wait for permission to solve problems\n\n - A doer mentality — you dig in and get stuff done\n\n - Experience managing engineers in a fast-paced, high-growth environment\n\n - Excellent communication skills in English.\n\n\n\n\nNICE TO HAVE'S\n\n - Fluency in additional European languages\n\n - Experience with AI/ML infrastructure\n\n - Background in enterprise security and compliance.\n\n\n\nCohere is committed to fair and transparent pay practices. The salary range listed for this role reflects the expected base compensation. Actual compensation offered will be determined by factors such as location, level, job-related knowledge, skills, education, and experience.\n\n - United States:\n   \n   - For candidates based in California, New York and Washington States, the compensation range is: $140,000 - $325,000 USD\n   \n   - For candidates based elsewhere in the US, the compensation range is: $120,000 – $275,000 USD\n\n - Canada:\n   \n   - For candidates in Canada, the Compensation Range is : $175,000 - $385,000 CAD\n\n\n\n\nFULL-TIME EMPLOYEES AT COHERE ENJOY THESE PERKS:\n\n - A weekly lunch stipend of $75/£75 or equivalent in your local currency for lunch.\n\n - Full health and dental benefits, including a separate budget for mental health.\n\n - RRSP matching, 401K, Pension Scheme.\n\n - 100% Parental Leave top-up for up to 6 months, for either parent.\n\n - Annual enrichment benefits:\n   \n   Arts \u0026 culture, fitness/wellness, quality time, and a workspace improvement credit.\n   \n   Education \u0026 learning stipend for conferences, courses, and coaching.\n\n - 6 weeks of paid vacation (30 working days!)\n\n - Budget for traveling to other offices if you are remote, plus an annual company offsite.\n\n\n\n\nHOW AND WHERE WE WORK:\n\n - Cohere is remote-friendly, but we also have offices in Toronto, London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul with more opening soon.\n\n - For those in the office: a daily lunch program, plenty of snacks, and regular community and social events.\n\n - For those not near an office: a co-working benefit so you can work alongside others in your city.\n\n - Everyone receives a $500 home office stipend to set up your workspace properly.\n   \n   \n\nIf any of the above doesn’t line up exactly with your experience, we still encourage you to apply. \n\n\nWe strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form https://docs.google.com/forms/d/12a6IrLdF3kI2nonKSr4tiFuz18rLQbaeYV-JM9L4o9Q/edit, and we will work together to meet your needs.\n\n\n\nWe may use AI-enabled tools to scr","salary_min":175000,"salary_max":385000,"location":"Canada","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"senior","tags":["payments","search","cloud","infrastructure"],"apply_url":"https://jobs.ashbyhq.com/cohere/6a6120d5-5e02-4811-99d9-6baf0b910e37/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-28T20:34:05.703Z","expires_at":"2026-09-29T13:31:56.992297Z","created_at":"2026-06-28T14:01:30.761519Z","updated_at":"2026-08-30T13:31:57.195425Z","company_name":"Cohere","company_slug":"cohere","company_logo_url":"https://www.google.com/s2/favicons?domain=cohere.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/49815b01-da50-4ed6-905c-a071c44a3cab"},{"id":"d9e245d1-a6c7-4071-9742-6a6ed60d8557","company_id":"a0000000-0000-0000-0000-000000000001","title":"Staff+ Research Engineer, RL Data Platform","slug":"staff-research-engineer-rl-data-platform-41e9b926","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 RL Data Platform team builds the systems that produce, move, and serve the human data Claude learns from: the interfaces humans use to give feedback, the pipelines that turn raw feedback into training signal, and the tooling researchers use to launch, monitor, and inspect data collection. Every RL run depends on a steady supply of high-quality data - human feedback, expert demonstrations, graded transcripts - and when a researcher has an idea for new data on Monday, our job is to make it collectable by Wednesday and in the training mix by Friday.\n This is a full-stack, ownership-heavy role on a small, senior team. You'll design and ship web interfaces used by thousands of expert annotators, build the backend services and data pipelines behind them, and work directly with RL researchers to understand what data they need and why. You'll scope your own projects, make architectural calls, and see them through to production. We're looking for engineers who treat researchers as their users, build for reliability first, and care as much about the shape of the data leaving the system as the UI going into it.\n Key responsibilities \n \n \n Design, build, and operate the feedback and data collection interfaces used by human annotators, domain experts, and internal researchers.\n \n Build and maintain the backend services, APIs, and pipelines that route model samples to humans and return structured feedback to training.\n \n Own the reliability, latency, and usability of systems that run continuously against live model endpoints.\n \n Partner with RL researchers to translate loosely specified data needs into well-scoped collection campaigns and the tooling to run them.\n \n Build dashboards, monitoring, and inspection tools so researchers can see data quality and throughput without asking an engineer.\n \n Identify and remove the bottlenecks between \"we want this data\" and \"it's in the training mix\".\n \n Minimum qualifications \n \n \n Strong full-stack engineering skills, with production experience in TypeScript/React on the frontend and Python on the backend.\n \n Experience designing and operating backend services and data pipelines that other teams depend on.\n \n A track record of owning projects end-to-end, from an ambiguous brief to something in production that people use.\n \n Comfort working directly with technical stakeholders whose needs change week to week, and the judgment to push back when something isn't worth building.\n \n Effective use of AI tools in your own day-to-day work.\n \n Care about the societal impacts of your work.\n \n Preferred qualifications \n \n \n Experience building annotation, labelling, evaluation, or other human-in-the-loop data tooling.\n \n Experience with RLHF, preference data, or other human-feedback pipelines for ML systems.\n \n Experience shipping researcher-facing or other expert-facing internal tools people love: interviewing users, hunting down friction, measurably improving the experience.\n \n Experience running experiments on data collection interfaces and using the results to improve data quality.\n \n Experience working with crowdworker or expert vendor platforms at scale.\n \n Familiarity with how LLMs are trained and evaluated.\n \n Representative projects \n \n \n Build an interface that lets a domain expert review a long agentic transcript, flag the step where things went wrong, and write a corrected continuation - with the result landing in a training-ready format.\n \n Rework the sampling path between our feedback interfaces and model endpoints to cut time-to-first-sample for annotators.\n \n Build a campaign launcher that lets a researcher stand up a new data collection effort (task, rubric, population, quality checks) without writing code.\n \n Instrument annotator behaviour to detect low-effort or adversarial work and surface it to the quality team automatically.\n \n Design the data model for a kind of feedback we haven't collected before, and ship the pipeline that gets it into the training mix.\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","salary_min":500000,"salary_max":850000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["llm","alignment","data-pipeline","agents","search","reinforcement-learning","research"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5404725008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-27T13:04:41Z","expires_at":"2026-09-29T13:30:36.347344Z","created_at":"2026-08-27T13:30:36.814345Z","updated_at":"2026-08-30T13:30:36.489598Z","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/d9e245d1-a6c7-4071-9742-6a6ed60d8557"},{"id":"0c041284-ced0-4d3e-bc76-8e40c29f632e","company_id":"d8e15a46-b80d-4228-8e7b-34f00357f377","title":"UX \u0026 Front End Engineer, AI ","slug":"ux-front-end-engineer-ai-192b5fbc","description":"Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale — unleashing the potential of businesses and people. The Elastic Search AI Platform, used by more than 50% of the Fortune 500, brings together the precision of search and the intelligence of AI to enable everyone to accelerate the results that matter. By taking advantage of all structured and unstructured data — securing and protecting private information more effectively — Elastic’s complete, cloud-based solutions for search, security, and observability help organizations deliver on the promise of AI.\n What is The Role \n \n The Elastic IT team is shifting beyond standard chat interfaces to craft the next frontier of generative and agentic AI experiences. We are looking for an innovative UX Engineer to join our team to bridge the gap between powerful AI capabilities and intuitive, delightful user interfaces that accelerate productivity across the entire organization. \n \n Our ideal candidate is a skilled front-end engineer with hands-on experience designing and building AI user experiences (e.g., streaming text, dynamic prompt workflows, agent reasoning visualizations, and multi-modal interaction patterns). In this role, you will leverage the latest AI technologies and the Elastic Stack (including ESRE, Agent Builder, Workflows and eUI) to build high-performance front-end applications for a suite of internal products and platforms. Driven by a user-centered mindset, you will act as a key collaborator between AI back-end engineers, product managers, and user groups to turn complex AI reasoning into seamless human-AI interactions. By shaping how enterprise users interact with generative AI, you will enable our global workforce while showcasing the boundary-pushing capabilities of Elastic's products. \n \n Are you ready to design and build the interfaces that supercharge enterprise productivity and prove what’s possible with Elastic? Join us to create AI user experiences that turn collective knowledge into instant action, empowering everyone at Elastic to achieve more. \n What You Will Be Doing \n \n \n AI UI/UX Design \u0026 Implementation: Translate complex generative and agentic AI processes into intuitive, responsive, and engaging front-end interfaces. \n \n Front-End Architecture: Design, build, and maintain front-end UI component libraries. These libraries should be scalable, accessible, and reusable and will be created for generative AI interactions. You will use modern frameworks like React and TypeScript. \n \n Human-AI Interaction Patterns: Prototype and implement novel interaction patterns for conversational AI, agent execution visibility (thought logs, tool calls), prompt systems, and rich dynamic outputs. \n \n Performance \u0026 Streaming Optimization: Optimize UI performance for real-time AI responses, managing token streaming latency, async state management, and optimistic UI updates. \n \n Enterprise Grounding \u0026 Integration: Connect front-end interfaces to internal services. This includes Retrieval Augmented Generation (RAG) endpoints. It also includes Elasticsearch Relevance Engine (ESRE) and agent orchestration APIs. \n \n User-Centered Collaboration: Partner closely with UX designers, product managers, and AI backend engineers to iteratively test and refine AI workflows based on real user feedback. \n \n Accessibility \u0026 Design Systems: Ensure all front-end interfaces strictly adhere to web accessibility standards (WCAG) and align seamlessly with Elastic's core design system (EUI). \n \n AI Observability \u0026 UX Analytics: Implement front-end tracking to monitor user satisfaction, prompt effectiveness , interaction latency, and interface usability. \n \n Documentation: Maintain comprehensive documentation for UI component systems, front-end architecture, and design pattern guidelines. \n \n What You Bring \n \n \n Proven Success in AI UX: Recent experience creating user interfaces for GenAI applications is important. This includes working with conversational interfaces, dynamic prompt builders, and complex agent workflows. \n \n Front-End Mastery: Deep expertise in modern TypeScript , JavaScript , React , HTML5, and CSS/Sass, with an emphasis on modular architecture. \n \n State Management \u0026 Streaming: Deep experience managing complex asynchronous UI state, WebSockets, and Server-Sent Events (SSE) for streaming LLM responses. \n \n UX/UI Design Foundations: Proficient background or active practice in user experience design, wireframing, design systems, and rapid prototyping. \n \n An Appetite to Master Elastic: A solid desire to learn and leverage the Elasticsearch Relevance Engine (ESRE) , Elastic UI (EUI), and the broader Elastic ecosystem. \n \n AI Framework \u0026 API Integration: Experience with integrating front-end systems with AI/LLM backend services and orchestration tools (e.g., LangGraph, REST/GraphQL APIs). \n \n Design System Integration: Experience extending and contributing to enterprise design systems ","salary_min":133200,"salary_max":210700,"location":"United States","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"mid","tags":["rag","api-design","llm","agents","search","generative-ai"],"apply_url":"https://jobs.elastic.co/jobs?gh_jid=8154995\u0026gh_jid=8154995","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-26T21:40:36Z","expires_at":"2026-09-29T13:39:18.19693Z","created_at":"2026-08-27T13:39:42.154555Z","updated_at":"2026-08-30T13:39:18.372298Z","company_name":"Elastic","company_slug":"elastic","company_logo_url":"https://www.google.com/s2/favicons?domain=www.elastic.co\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/0c041284-ced0-4d3e-bc76-8e40c29f632e"},{"id":"a60887bd-18b6-4819-b8ac-a8ce688f7d3f","company_id":"a0000000-0000-0000-0000-000000000003","title":"Machine Learning Research Scientist, Evaluations","slug":"machine-learning-research-scientist-evaluations-47ca5c35","description":"Scale works with the industry's leading AI labs to provide high quality data and accelerate progress in GenAI research. We are looking for Research Scientists and Research Engineers with expertise in LLM post-training (SFT, RLHF, reward modeling) and evaluation. This role is on the evaluation pod within the GenAI Research Organization and will focus on building benchmarks and diagnosing model failure modes in both text and multimodal modalities.\n In this role, you will develop rigorous evaluations and diagnostic methods that reveal where frontier models fail and why. You will collaborate with researchers and engineers to define best practices in evaluation-driven AI development. You will also partner with top foundation model labs to translate failure analysis into technical and strategic input on the next generation of generative AI models.\n You will: \n \n Analyze model behavior to identify, characterize, and diagnose failure modes in frontier LLMs and Agents.  You’ll identify everything from capability gaps and reasoning errors to robustness and alignment issues, all focusing on RCA.\n Design and build benchmarks and evaluation methods that measure LLM capabilities in both text and multimodal modalities.\n Apply post-training expertise (SFT, RLHF, reward modeling) to connect observed failures to the data and training interventions that address them.\n Publish research findings in top-tier AI conferences.\n \n Ideally you’d have: \n \n Ph.D. or Master's degree in Computer Science, Machine Learning, AI, or a related field.\n Deep understanding of deep learning, reinforcement learning, and large-scale model fine-tuning.\n Experience with post-training techniques such as RLHF, preference modeling, or instruction tuning, and with LLM evaluation or benchmark development.\n Excellent written and verbal communication skills.\n Published research in areas of machine learning at major conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, etc.) and/or journals.\n Previous experience in a customer facing role.\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 levels at Scale; it will be determined during the interview process based on work location and additional factors, including job-related skills, experience, qualifications, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You'll also receive benefits including, but not limited to: comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend. \n Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco, New York, Seattle is:\n $180,600 — $225,750 USD \n PLEASE NOTE:  Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants. \n About Us: \n At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Ernst \u0026 Young, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. We are expanding our team to accelerate the development of AI applications. \n We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status.  \n We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at accommodations@scale.com. Please see the United States Department of Labor's Know Your Rights poster for additional information. \n We comply with the United States Department of Labor's Pay Transparency provision .  \n PLEASE NOTE: We co","salary_min":180600,"salary_max":225750,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["deep-learning","fine-tuning","generative-ai","reinforcement-learning","search","nlp","llm","evaluation"],"apply_url":"https://job-boards.greenhouse.io/scaleai/jobs/4728014005","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-26T18:51:42Z","expires_at":"2026-09-29T13:31:40.355249Z","created_at":"2026-08-27T13:31:38.7307Z","updated_at":"2026-08-30T13:31:40.501539Z","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/a60887bd-18b6-4819-b8ac-a8ce688f7d3f"},{"id":"82dec910-1062-4275-8ca4-cb2a591a1dd1","company_id":"5d6de1f6-4d6c-463b-8a2b-a5caeadb97b4","title":"Senior Software Engineer - Build, NYC","slug":"senior-software-engineer-build-nyc-e267b790","description":"Astronomer empowers data teams to bring mission-critical software, analytics, and AI to life and is the company behind Astro, the industry-leading unified DataOps platform powered by Apache Airflow®. Astro accelerates building reliable data products that unlock insights, unleash AI value, and powers data-driven applications. Trusted by more than 800 of the world's leading enterprises, Astronomer lets businesses do more with their data. To learn more, visit  www.astronomer.io http://www.astronomer.io.\n\n\n\n\nABOUT THIS ROLE\n\nApache Airflow is one of the most popular open-source data platform tools. It powers the data platforms at nearly every large company and fast-growing startups: Airbnb, Uber, OpenAI, Anthropic, Nike, Capital One, Disney all use Airflow extensively. At Astronomer, we’re the largest contributors to the project and are building commercial products around Airflow to make it easier to use, run, and scale.\n\n\n\nWe’re in a unique position: as the company behind Apache Airflow, we see data as it moves across entire organizations—from raw ingestion to production dashboards, machine learning models, and AI products. Leveraging this vantage point, our R\u0026D team is developing a global context layer for data—an intelligence layer that powers search and discovery, code generation for data and analytics, and automated root cause analysis. LLMs are already quite good at writing Python and SQL code against data platforms; we think this context layer will give them the metadata necessary for data practitioners everywhere to use LLMs effectively.\n\n\n\nAs a Software Engineer on this team, you’ll help design and build this foundation and the applications around it. You’ll work on some of the hardest and most exciting challenges in data—search, information retrieval, and AI for data pracitioners—while collaborating with a small, highly skilled team that values velocity, creativity, and impact. This role sits at the intersection of applied research, software engineering, and product: we think it takes someone who can work across the stack to build, release, and scale products successfully in this space.\n\n\n\nHybrid Work Model: For this role, you will embrace a flexible hybrid work model with at least 3 days per week in our New York City office.\n\n\n\n\n\nWHAT YOU GET TO DO:\n\n - Shape the future of AI for data engineering - build intelligent systems that understand, reason about, and optimize the flow of data across entire organizations.\n\n - Design and engineer the brain of Astronomer’s context layer, crafting components that power data modeling, semantic search, retrieval, and code generation.\n\n - Push the boundaries of applied AI - experiment with LLMs, embeddings, and cutting-edge retrieval techniques to create developer tools that deliver insights to you and our customers.\n\n - Turn research into reality - work side by side with R\u0026D and product teams to bring early AI concepts to life in the product experience.\n\n - Solve high-impact information retrieval and search challenges at a global scale, leveraging Astronomer’s unparalleled visibility into data pipelines across industries.\n\n - Influence the technical vision and architecture for the next generation of AI-driven data products.\n\n - Represent Astronomer in the community - through open-source contributions, technical talks, and publications that showcase our leadership in AI and data innovation.\n\n\n\n\nWHAT YOU BRING TO THE ROLE:\n\n - 5-8 years of software engineering experience with Python or Go\n\n - Empathy for users, and a deep interest in improving the workflows of data professionals.\n\n - Familiarity with early-stage product development; comfortable working with ambiguity in a fast-changing field.\n\n - Experience with LLMs, vector databases, embeddings, or other applied AI areas—or a strong desire to dive in.\n\n - A creative, experimental mindset: you enjoy exploring uncharted areas, validating hypotheses, and learning through iteration.\n\n - Strong collaboration and communication skills—you can explain complex systems clearly to both technical and non-technical audiences.\n\n - A collaborative approach and comfort working in an evolving, research-driven environment where ideas move quickly.\n\n\n\n\nBONUS POINTS IF YOU HAVE:\n\n - A passion for AI systems for data, developer tools, or machine learning infrastructure.\n\n - Familiarity with Apache Airflow or other orchestration tools.\n\n - Demonstrated contributions to open source projects.\n\n - Experience in search, IR, or large-scale data infrastructure.\n\n - Exposure to early-stage startups or R\u0026D organizations where ambiguity is the norm.\n\n - Experience building out agentic systems on top of frontier models.\n\n\n\nThe estimated total compensation for this role ranges from $210,000 - $250,000 based on leveling and geography, along with an equity component and a comprehensive benefits package. This range is merely an estimate; actual compensation may deviate from this range based on skills, experience, and qualific","salary_min":210000,"salary_max":250000,"location":"New York, NY","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["data-pipeline","code-generation","llm","search","embeddings","agents"],"apply_url":"https://jobs.ashbyhq.com/astronomer/3c72cd98-3493-4d30-8897-dc5e836db67e/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-26T08:13:00.068Z","expires_at":"2026-09-29T13:47:14.977036Z","created_at":"2026-08-26T13:47:11.23022Z","updated_at":"2026-08-30T13:47:15.105522Z","company_name":"Astronomer","company_slug":"astronomer","company_logo_url":"https://www.google.com/s2/favicons?domain=astronomer.io\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/82dec910-1062-4275-8ca4-cb2a591a1dd1"},{"id":"42d0b3d6-fb09-4db9-9c43-b451572a2005","company_id":"da5cfe83-4fb2-4ab3-9392-94069a77ae59","title":"Staff/Senior Staff Software Engineer, Agentic Search","slug":"senior-staff-software-engineer-agentic-search-c4676bc8","description":"Ironclad is the leading AI contracting platform that transforms agreements into assets. Contracts move faster, insights surface instantly, and agents push work forward, all with you in control.  Whether you’re buying or selling, Ironclad unifies the entire process on one intelligent platform, providing leaders with the visibility they need to stay one step ahead. That’s why the world’s most transformative organizations, from Rivian to the World Health Organization and the Associated Press, trust Ironclad to accelerate their business.\n\n\nWe’re consistently recognized as a leader in the industry: a Leader in the Forrester Wave and Gartner Magic Quadrant for Contract Lifecycle Management, a Fortune Great Place to Work, and one of Fast Company’s Most Innovative Workplaces. Ironclad has also been named to Forbes’ AI 50  and Business Insider’s list of Companies to Bet Your Career On. We’re backed by leading investors including Accel, Y Combinator, Sequoia, BOND, and Franklin Templeton. For more information, visit www.ironcladapp.com http://www.ironcladapp.com or follow us on LinkedIn.\n\n\n\n\nABOUT THE ROLE\n\nIronclad's Intelligence Platform team owns Agent Assistant, Conversational Search, and Content Understanding — the systems that help customers and AI agents understand, find, and act on the right contract information. These are the flagship AI capabilities of our product, built and operated by a combined team of ML and ML infrastructure engineers.\n\nWe have multiple roles open, and are hiring a range of levels — Staff and Senior Staff. As a Staff or Senior Staff Engineer, Agentic Search, you'll own the architecture that combines LLMs and retrieval systems to answer complex, ambiguous questions about a customer's contracts, and you'll set the technical direction that other engineers across the AI organization build on. You'll partner closely with product, applied science, and engineering leaders to raise the company's search quality bar, and you'll bring the technical depth and eval-driven rigor to turn ambiguous problems into shipped, measurable improvements. Scope and ownership will be calibrated to level.\n\n\n\n\nWHAT YOU'LL DO\n\n - Own agentic search architecture. Design and evolve the systems that combine LLMs and retrieval to produce optimal answers to complex or ambiguous questions.\n\n - Drive eval-driven development. Design and run the benchmarks and experiments that measure search quality, and use that feedback to continuously improve the system.\n\n - Raise the search quality bar. Contribute to and influence the company's overall search quality standard.\n\n - Own content understanding and ingestion. Turn raw documents into processed data that retrieval systems can consume, by building and using NLP/LLM models and pipelines.\n\n - Set technical direction. Define architectural decisions and technical direction that other engineers across the AI organization build on.\n\n\nQUALIFICATIONS\n\n - 10+ years building production systems, with a substantial portion in search, information retrieval, content understanding, or recommendation systems at meaningful scale.\n\n - Demonstrated depth in one of: learned/hybrid retrieval (lexical + vector + reranking), query understanding/NLU pipelines, or production LLM agent systems — ideally more than one.\n\n - Experience with search frameworks (Elasticsearch or equivalent — Solr, Vespa, OpenSearch; embedding search) in production, including relevance tuning and reranking.\n\n - Fluency with modern LLM APIs and multi-provider orchestration (Anthropic, OpenAI, Google) — reasoning about token budgets, provider-specific tool-calling semantics, and prompt-caching trade-offs.\n\n - Experience building eval-driven workflows — offline benchmarks, regression detection, structured A/B comparison — as opposed to shipping and hoping.\n\n - Strong autonomy, ownership, and technical leadership across teams, including mentoring senior engineers and driving architectural decisions.\n\n - Comfortable operating in a dynamic, fast-paced, outcome-driven environment.\n\n\nGREAT TO HAVE\n\n - Hands-on experience with post-training algorithms and infrastructure, including SFT and RL.\n\n - Experience with content understanding and/or information retrieval in structured-document-heavy domains.\n\n - Prior work on RAG systems involving data sources in different formats (Google Docs, PDFs, DOCX, etc.).\n\n\n\n\n\nBASE SALARY RANGES\n\n - Staff: $188,000 - $235,000\n\n - Senior Staff: $220,000 - $270,000\n\nThe base salary range represents the minimum and maximum of the salary range for this position based at our San Francisco headquarters. The actual base salary offered for this position will depend on numerous factors, including individual proficiency, anticipated performance, and the location of the selected candidate. Our base salary is just one component of Ironclad's competitive total rewards package, which also includes equity awards (a new hire grant, along with opportunities for additional awards throughout ","salary_min":220000,"salary_max":270000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["llm","nlp","rag","agents","search"],"apply_url":"https://jobs.ashbyhq.com/ironcladhq/4be2d35a-9aa0-415c-ba13-30da080158ad/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-25T20:47:44.879Z","expires_at":"2026-09-29T13:41:24.070143Z","created_at":"2026-08-26T13:41:23.863704Z","updated_at":"2026-08-30T13:41:24.20808Z","company_name":"Ironclad","company_slug":"ironclad","company_logo_url":"https://www.google.com/s2/favicons?domain=ironcladapp.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/42d0b3d6-fb09-4db9-9c43-b451572a2005"},{"id":"b9aa4c99-030d-4e40-9e6f-9761f83b7938","company_id":"308b7777-69e1-49db-ad79-3912d6c6e648","title":"Staff Software Engineer, Marketplace Acquisition","slug":"staff-software-engineer-marketplace-acquisition-e55d1f4b","description":"Our Mission \n Healthcare should work for patients, but it doesn’t. In their time of need, they call down outdated insurance directories. Then wait on hold. Then wait weeks for the privilege of a visit. Then wait in a room solely designed for waiting. Then wait for a surprise bill. In any other consumer industry, the companies delivering such a poor customer experience would not survive. But in healthcare, patients lack market power. Which means they are expected to accept the unacceptable. \n  \n Zocdoc’s mission is to give power to the patient. To do that, we’ve built the leading healthcare marketplace that makes it easy to find and book in-person or virtual care in all 50 states, across +200 specialties and +12k insurance plans. By giving patients the ability to see and choose, we give them power. In doing so, we can make healthcare work like every other consumer sector, where businesses compete for customers, not the other way around. In time, this will drive quality up and prices down.  \n  \n We’re 18 years old and the leader in our space, but we are still just getting started. If you like solving important, complex problems alongside deeply thoughtful, driven, and collaborative teammates, read on. \n  \n Your Impact on our Mission \n As a Staff Software Engineer on the Marketplace Acquisition team at Zocdoc, you'll turn ideas into reality fast — prototyping, iterating, and shipping high-impact experiences that help patients discover and access care. You'll own the systems behind Zocdoc's patient acquisition and engagement, keeping everything at the top of the funnel stable, scalable, and fast. That means driving the evolution of our SEO infrastructure, which powers thousands of patient-facing pages; iterating on provider profile and practice page designs to create seamless, trustworthy experiences; and building the APIs and services that make patient-facing data fast and reliable at scale.\n You'll also shape how we build. Zocdoc is making a company-wide push to drive engineering through the lens of AI, and you'll be an active participant in it — bringing AI into your day-to-day development, contributing to the standards, guardrails, and review practices the organization is building, and raising the ceiling on what a small team can ship. Multiplying the output of the engineers around you is as much a part of this role as the code you write yourself.\n You'll work closely with Design, Product, and Marketing to align on strategy and deliver measurable impact, all while fostering a culture of technical excellence, mentorship, and innovation that keeps Zocdoc at the forefront of healthcare technology.\n  \n You’ll enjoy this role if you are… \n \n Driven by the opportunity to impact healthcare positively, our mission is to create a seamless, efficient, and deeply human experience\n Product-driven with a relentless focus on user needs\n Passionate about engineering excellence, with a strong focus on promoting best practices in code quality, testing, and long-term maintainability across teams and the organization\n Demonstrates strong ownership of their technical domain and invests in mentoring others by sharing knowledge and best practices across teams\n \n  \n Your day to day is… \n \n Leading the technical direction of Zocdoc’s acquisition platform, enhancing system stability and user experience. Guiding technical discussions, ensuring decisions are scalable and dependable, and aligning infrastructure with the team’s broader mission\n Building scalable APIs and microservices that power frictionless data access and informed patient decision-making\n Collaborating with design counterparts to iterate on user experiences and improve the overall customer journey\n Proactively engaging with product counterparts to align on vision, strategy, and execution, ensuring projects stay on track and deliver business value\n Mentoring engineers and driving team excellence through coding standards, automation, and continuous improvement\n Leading bold innovation initiatives centered on AI, leveraging emerging data sources and cutting-edge architectures to position Zocdoc as a leader in healthcare technology\n \n  \n You’ll be successful in this role if you have… \n \n Owned and evolved complex, user-facing platforms end-to-end, balancing rapid delivery with long-term technical vision to ensure scalable, maintainable, and impactful experiences\n Designed and built performant systems with search engine optimization (SEO) and page speed as top priorities, ensuring fast, reliable, and discoverable patient experiences at scale\n Architected full-stack solutions spanning backend APIs, AWS-based infrastructure, and React systems that are modular, reusable, and leveraged across teams\n Integrated with third-party data sources and content management systems, such as Contentful, to deliver dynamic, flexible, and content-rich user experiences\n Leveraged large language model (LLM) systems to transform raw user data into actionable in","salary_min":180000,"salary_max":265000,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"lead","tags":["healthcare","llm","search","microservices"],"apply_url":"https://job-boards.greenhouse.io/zocdoc/jobs/8082085","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-24T19:28:42Z","expires_at":"2026-09-29T13:49:10.506575Z","created_at":"2026-08-25T18:33:36.50581Z","updated_at":"2026-08-30T13:49:10.636727Z","company_name":"ZocDoc","company_slug":"zocdoc","company_logo_url":"https://www.google.com/s2/favicons?domain=zocdoc.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/b9aa4c99-030d-4e40-9e6f-9761f83b7938"},{"id":"3707734c-f7ec-4eee-bd06-3bd04c3d3355","company_id":"52f44519-9f93-4eac-ae0b-8be13e385ebe","title":"Cloud DevOps Engineer","slug":"cloud-devops-engineer-c42c2a76","description":"CLOUD DEVOPS ENGINEER\n\n\n\nYou'll build the cloud infrastructure that turns the open web into data — the platform beneath Firecrawl's crawling, scraping, and search products. We need engineers who can make that foundation — Kubernetes, storage, networking, deployments — fast, reliable, and cheap at web scale. You'll own real infrastructure from day one — not tickets in a backlog.\n\n \n\nSalary Range: $246,000–$271,000/year\n\nEquity Range: Competitive equity — details shared during the process.\n\nLocation: San Francisco, CA (Onsite)\n\nJob Type: Full-Time \n\nExperience: 5+ years in DevOps, Platform Engineering or Cloud Infrastructure \n\nVisa: Must be legally authorized to work in the United States. 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 170k+ 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, build, and operate GCP and on-prem infrastructure behind Firecrawl's products\n\n - Run large stateful and high-throughput workloads on Kubernetes — search clusters, crawling fleets, queues, and databases — with zero-downtime upgrades\n\n - Own CI/CD, Infrastructure as Code, automations, and containerized deployments across the platform\n\n - Drive down infrastructure cost per request while traffic and data volume grow\n\n - Build the observability that keeps latency, throughput, and reliability predictable and define the SLIs, SLOs, and customer-facing SLAs we hold ourselves to.\n\n - Build and support our enterprise controls — SSO/SAML, RBAC, audit logging, tenant isolation, private networking, and the infrastructure behind SOC 2 and customer security reviews\n\n - Work directly with product and search engineers to productionize new services, retrieval, and ML workloads\n\n - Own the incident lifecycle with engineers — from on-call and triage process to postmortems and resolution.\n\n\n\n\nWHAT WE'RE LOOKING FOR\n\n - You've operated stateful distributed systems on Kubernetes at real scale — not just stateless services\n\n - You have deep experience with a major cloud (e.g. GCP, AWS, Azure), Docker, and Terraform; MLOps or ML-serving infrastructure experience (GPU workloads, model deployment pipelines) is a plus\n\n - You've run large-scale, data-heavy systems in production — search platforms, crawling or ingestion pipelines, or comparable. Hands-on experience operating Vespa https://github.com/vespa-engine/vespa is a strong plus.\n\n - You care about latency, cost, and reliability in equal measure\n\n - Experience with security and compliance infrastructure (SSO/SAML, audit logging, network isolation, SOC 2) is a strong plus — especially for the Core Platform focus\n\n - You're comfortable owning ambiguous problems and turning them into shipped infrastructure\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 — $246,000–$271,000/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% fo","salary_min":246000,"salary_max":271000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["llm","distributed-systems","agents","mlops","search","cloud","devops"],"apply_url":"https://jobs.ashbyhq.com/firecrawl/fe538f2b-7dd5-4d8d-941e-f8014a911652/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-20T22:04:31.381Z","expires_at":"2026-09-29T13:45:44.202795Z","created_at":"2026-08-25T18:32:19.567715Z","updated_at":"2026-08-30T13:45:44.333747Z","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/3707734c-f7ec-4eee-bd06-3bd04c3d3355"},{"id":"95641443-c7f1-443a-b4d7-ef763360667d","company_id":"52f44519-9f93-4eac-ae0b-8be13e385ebe","title":"Machine Learning Engineer","slug":"machine-learning-engineer-a0756d7f","description":"MACHINE LEARNING ENGINEER\n\n \n\nYou'll build the ML behind Firecrawl — the models and the systems that serve them. That starts with search: training and shipping the ranking and relevance models for one of our fastest-growing products, then extending that work across extraction quality and LLM-driven features. You'll also own how we measure: A/B testing launches and building the experimentation frameworks the whole team ships against. If you ship models into production — whether your title says ML engineer or data scientist — this is for you.\n\n \n\nSalary Range: $250,000–$290,000/year\n\nEquity Range: Competitive equity — details shared during the process.\n\nLocation: San Francisco, CA (Onsite)\n\nJob Type: Full-Time \n\nExperience: 3+ years building ML or data-heavy systems in production \n\nVisa: Must be legally authorized to work in the United States. 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 170k+ 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 - Improve ranking and relevance for Firecrawl Search — from feature engineering to model training to production\n\n - Build and tune models for learning-to-rank, query understanding, and LLM-driven retrieval\n\n - Extend ML across Firecrawl's products — extraction quality, content classification, and evaluation of LLM-driven features\n\n - Mine query logs and behavioral data at scale to find where our products win and where they fail\n\n - Build the data pipelines that turn web-scale crawl and query data into training data and features\n\n - Work hands-on with platform, search engineers and cloud DevOps to get models running fast and cheap in production\n\n - Design and formulate our testing strategy — the A/B testing frameworks and offline evaluation the team ships against\n\n - Partner on product launches across Firecrawl: define success metrics, run the experiments, and make the ship/no-ship call on evidence\n\n - Report on how releases perform post-launch and turn the findings into the next iteration\n\n\n\n\nWHAT WE'RE LOOKING FOR\n\n - You've shipped ML models into production systems and owned them after launch — deploying, monitoring, and retraining them, not handing them off\n\n - You have real ranking or relevance-modeling experience — learning-to-rank, recommendations, or search quality\n\n - You're comfortable in large, data-heavy systems: query logs, pipelines, and datasets that don't fit in memory\n\n - You write production-quality code (Python at minimum) and can work inside a real backend codebase\n\n - You're rigorous about measurement — you've designed and analyzed A/B tests and know when a lift is real\n\n - You can communicate results clearly to the team — what shipped, what moved, and what to do next\n\n\n\n\nNICE TO HAVE\n\n - MLOps experience — MLflow, experiment tracking, model registries, or feature stores; Kubernetes is a plus\n\n - Experience building or standardizing an experimentation framework at a previous company\n\n - Experience with embedding models, vector retrieval, or LLM-based relevance evaluation\n\n - Experience evaluating LLM outputs at scale — quality scoring, structured-extraction accuracy, or agent behavior\n\n - Spark or similar large-scale data processing experience\n\n\n\n\nWHAT WE'RE NOT LOOKING FOR\n\n - A pure statistician or analyst who needs an engineering team to productionize their work\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 — $250,000–$290,000/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 l","salary_min":250000,"salary_max":290000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["search","data-pipeline","embeddings","mlops","agents","llm","machine-learning"],"apply_url":"https://jobs.ashbyhq.com/firecrawl/72f9dc1d-65db-48c9-b3d9-c6ccdb997006/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-20T21:40:10.101Z","expires_at":"2026-09-29T13:45:43.966664Z","created_at":"2026-08-25T18:32:19.563403Z","updated_at":"2026-08-30T13:45:44.23876Z","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/95641443-c7f1-443a-b4d7-ef763360667d"},{"id":"ac071719-be76-48b4-a476-7a614bc11915","company_id":"2ca4efa5-edc2-4352-a597-ea27086e1e5b","title":"Machine Learning Engineer II, Ads - Response Prediction","slug":"machine-learning-engineer-ii-ads-response-prediction-3e47bda7","description":"We're transforming the grocery industry \n At Instacart, we invite the world to share love through food because we believe everyone should have access to the food they love and more time to enjoy it together. Where others see a simple need for grocery delivery, we see exciting complexity and endless opportunity to serve the varied needs of our community. We work to deliver an essential service that customers rely on to get their groceries and household goods, while also offering safe and flexible earnings opportunities to Instacart Personal Shoppers. \n Instacart has become a lifeline for millions of people, and we’re building the team to help push our shopping cart forward. If you’re ready to do the best work of your life, come join our table.\n Instacart is a Flex First team \n There’s no one-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their best work—whether it’s from home, an office, or your favorite coffee shop—while staying connected and building community through regular in-person events. Learn more about our flexible approach to where we work. \n Overview \n About the Role: \n As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart's ads systems. You will use machine learning to devise and refine solutions in crucial areas such as ads selection, ranking, bidding, and auction across all of Instacart’s consumer facing surfaces and Ads Ecosystems. You will actively contribute to initiatives, assisting in all stages of ML projects from the initial concept, through prototyping and experimentation, to final launch.\n About the Team: \n The Ads Response Prediction team owns systems, algorithms and ML models to ensure a relevant and engaging Ads experience to customers of all the platforms powered by Instacart. This includes search and exploration retrieval systems, Sequential Modeling and Generative Retrieval systems, LLM integrations, relevance models, pCTR models, bidding models and Foundation Models. The team optimizes for an efficient marketplace to ensure customers see ads that help them try new products/brands, advertisers boost their product sales for a good return on investment, and instacart generates the deserving revenue as well.\n  \n About the Job: \n \n Design, develop, and deploy machine learning solutions including data pipelines, model architectures and serving integrations to tackle practical challenges in the ads organization.\n Formulate and scope ambiguous modeling problems from first principles. Translate business observations (e.g., miss-calibration patterns, cold-start underperformance) into well-defined ML research directions with clear evaluation criteria.\n Collaborate closely with product managers, data scientists, and infrastructure engineers to deeply understand business needs and create impactful ML applications.\n Push the envelope on our operational efficiency by continually refining and advancing our algorithms and models.\n Publish and present findings internally. Contribute to the team’s culture of technical rigor through design reviews, paper sharing, and experiment retrospectives.\n \n  \n About You: \n Minimum Qualifications: \n \n Have a graduate degree (masters or PhD) in machine learning, statistics, computer science, information retrieval, or a closely related quantitative field.\n Have strong programming skills and fluency in data manipulation (SQL, Spark, Pandas) and Machine Learning (classical ML and Deep Learning) tools.\n Have strong analytical skills and problem-solving ability.\n Are a strong communicator who can collaborate with diverse stakeholders across all levels.\n \n  \n Preferred Qualifications: \n \n Have 1-2 years of industry experience using machine learning to solve real-world problems with large datasets.\n Knowledge of sequential modeling, Transformer architecture and Foundation Model.\n Familiarity with LLM integrations, agentic workflow and productivity tooling.\n Experience in building large scale online recommendation systems.\n \n #LI-Remote \n Instacart provides highly market-competitive compensation and benefits in each location where our employees work. This role is remote and the base pay range for a successful candidate is dependent on their permanent work location. Please review our Flex First remote work policy here . Currently, we are only hiring in the following provinces: Ontario, Alberta, British Columbia, and Nova Scotia.\n Offers may vary based on many factors, such as candidate experience and skills required for the role. Additionally, this role is eligible for a new hire equity grant as well as annual refresh grants. Please read more about our benefits offerings here .\n For Canadian based candidates, the base pay ranges for a successful candidate are listed below.\n CAN\n $154,000 — $162,500 CAD","salary_min":154000,"salary_max":162500,"location":"Remote (Canada)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"junior","tags":["agents","generative-ai","data-pipeline","fine-tuning","search","llm","deep-learning","machine-learning"],"apply_url":"https://instacart.careers/job/?gh_jid=8143263","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-20T19:14:19Z","expires_at":"2026-09-29T13:39:09.256769Z","created_at":"2026-08-25T18:28:59.889259Z","updated_at":"2026-08-30T13:39:09.402003Z","company_name":"Instacart","company_slug":"instacart","company_logo_url":"https://www.google.com/s2/favicons?domain=www.instacart.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/ac071719-be76-48b4-a476-7a614bc11915"},{"id":"30922887-a8eb-4d71-b922-a837aef04ace","company_id":"4c0fefc3-173a-4227-a823-4d67d3e70ff0","title":"Senior Research Engineer","slug":"senior-research-engineer-9f57f845","description":"Persons in these roles are expected to work from our offices in Seattle. On-site requirements vary based on position and team. If you have questions about on-site work arrangements for this role, please ask your recruiter.\n Our base salary range is $174,240 - $261,360, and in addition we have generous bonus plans to provide a competitive compensation package. \n Who You Are: \n OlmoEarth is growing — more partners, more use cases, and a platform that is evolving quickly. We are looking for a Senior Research Engineer who can collaborate with our partners to tailor the OlmoEarth models to a wide range of specific applications across multiple domains. \n Who We Are:  \n OlmoEarth is an open, end-to-end platform built around a family of foundation models for Earth observation. The platform enables users to create custom fine-tuned models to detect and classify novel geospatial features, handling the full loop: imagery acquisition, annotation, distributed model training and inference, and visualization.\n Our partners span some of the most respected institutions working on wildfire risk, crop mapping, mangrove conservation, and forest protection. OlmoEarth sits within the AI for the Planet group at the Allen Institute for AI, a small, mission-driven team working on conservation, food security, disaster resilience, and climate solutions.\n Learn more: https://allenai.org/olmoearth \n What We Believe \n \n The mission is the point. OlmoEarth exists to put powerful Earth observation tools into the hands of people working on conservation, food security, and climate. Every partner engagement this role supports is connected to that goal. If it matters to you that your day-to-day work adds up to something larger, you are in the right place.\n Good operations are invisible and indispensable. When coordination, documentation, and follow-through are working well, the whole team moves faster and partners have a better experience. This role is the engine behind that.\n Our partners are the signal. We learn what to build and how to improve by staying close to the people using the platform. The feedback, patterns, and friction you surface in this role directly shapes what the team works on next.\n In-person matters. A lot of the best work on this team happens in quick, unplanned conversations between engineering, research, and partnerships. We are mostly in the office because that is where this kind of collaboration happens naturally.\n Say what you think. We make better decisions when people share what they are actually seeing — whether that is a process that is not working, a partner need we are missing, or an idea for doing something differently. Everyone here is still learning, and we like it that way.\n \n Your Next Challenge: \n You will work with partners to deploy OlmoEarth for their use cases. This will require you to move fluidly across the entire OlmoEarth team, working with partners, engineers and researchers. You will make meaningful contributions to all the components of OlmoEarth’s infrastructure (from the finetuning code to model pretraining to our rslearn backend).\n Use case enablement \n \n Collaborate closely with partners to deploy OlmoEarth models in challenging contexts. This will prioritize contexts and partners for which we don’t have immediate solutions or there’s an opportunity to standardize a high quality approach for common use cases.. \n Explore novel use cases for the OlmoEarth models (e.g. post-hoc addition of new modalities, effectively leveraging embeddings in different contexts) which can unlock new use cases and partners.\n \n Partner Communications \u0026 Coordination \n \n As part of model development, maintain communications with key partners to ensure their success using the OlmoEarth platform.\n Communicate  internally  so that partner needs are clearly understood by the OlmoEarth machine learning research, engineering and partnership teams. \n \n Product Improvement and Research \n \n Work closely with the engineering and partnerships  team to feed lessons you learn when deploying models into our infrastructure. This includes improvements to rslearn, OlmoEarth Studio. \n Collaborate with the research team to identify and fix issues with the OlmoEarth models preventing their deployment in specific important applications. \n Continually update our model adaptation approaches to improve model performance for all our partners. This includes updating our fine-tuning approaches, developing recipes for new applications and improving the UI so that modelling trade-offs can be better understood by users. \n Support agent evaluations and development.\n \n What You’ll Need: \n Required\n \n 2+ years of experience deploying machine learning solutions. This covers the full stack of machine learning, including understanding the business case and requirements, training models, and deploying them at scale.\n Technical experience using machine learning tools. This includes fluency in PyTorch, experience debugging traini","salary_min":174240,"salary_max":261360,"location":"Seattle, WA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["pytorch","search","robotics","pre-training","fine-tuning","generative-ai","research"],"apply_url":"https://job-boards.greenhouse.io/thealleninstitute/jobs/8140098","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-20T18:47:01Z","expires_at":"2026-09-29T13:47:18.072158Z","created_at":"2026-08-25T18:32:53.66843Z","updated_at":"2026-08-30T13:47:18.202634Z","company_name":"Allen Institute for AI","company_slug":"allen-institute-for-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=allenai.org\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/30922887-a8eb-4d71-b922-a837aef04ace"},{"id":"d5aae1c9-cae9-47d5-9fce-478d44a8cb20","company_id":"4ed3e523-b627-46ed-8dae-04c2ea823be2","title":"Research Scientist/Research Engineer, Reinforcement Learning ","slug":"research-scientistresearch-engineer-reinforcement-learning-2476fcd9","description":"Jump Trading Group is committed to world-class research. We empower exceptional talents in Mathematics, Physics, and Computer Science to seek scientific boundaries, push through them, and apply cutting-edge research to global financial markets. Our culture is unique. Constant innovation requires fearlessness, creativity, intellectual honesty, and a relentless competitive streak. We believe in winning together and unlocking unique individual talent by incentivizing collaboration and mutual respect. At Jump, research outcomes drive more than superior risk-adjusted returns. We design, develop, and deploy technologies that change our world, fund start-ups across industries, and partner with leading global research organizations and universities to solve problems.\n Our team is a group of quantitative researchers, engineers, and ML experts leading reinforcement learning research and trading at Jump. Our mission is to combine emerging techniques and original research to learn optimal decision-making policies from financial market data and monetize them globally. We are building the future of ML-powered trading through breakthrough reinforcement learning, and we're looking for an exceptional Research Scientist/Research Engineer to join our team.\n What You’ll Do \n As a Research Scientist/Research Engineer working on RL, you'll be at the forefront of applying reinforcement learning to markets. You'll conduct original research and own the systems that turn it into production trading: designing and evaluating policy architectures, reward formulations, and objective horizons with rigorous out-of-sample benchmarking; partnering with trading and research teams to source, integrate, and validate their alpha signals within the RL framework; ensuring simulation fidelity against live trading by modeling market microstructure, fill dynamics, liquidity, and latency; building efficient tooling to store, process, and analyze very large volumes of market and signal data; and communicating findings to technical and trading audiences. This isn't incremental optimization; we're pushing the boundaries of what reinforcement learning can do at scale, where your improvements directly impact live trading.\n Other duties as assigned or needed. Skills You’ll Need \n \n 5+ years of experience developing reinforcement learning and/or deep learning systems with measurable impact in industry and/or academia\n Depth in reinforcement learning, including experience designing reward formulations, policy architectures, and evaluation, and taking RL methods from research into production\n Proficiency in Python and/or C++\n Familiarity with ML libraries/frameworks such as PyTorch (preferred), TensorFlow, and/or JAX\n Strong foundation in mathematics and statistics\n PhD or Master's degree in Computer Science, Machine Learning, Robotics (or a related subject)\n Strong publication record at ICML, ICLR, AAAI, NeurIPS, CVPR, or equivalent\n Ability to thrive in a collaborative, team-oriented environment\n Creative thinkers who are driven, self-motivated, and eager to solve challenging problems\n Reliable and predictable availability\n Excellent written and verbal communication skills in English\n Benefits \n \n Discretionary bonus eligibility \n Medical, dental, and vision insurance \n HSA, FSA, and Dependent Care options \n Employer Paid Group Term Life and AD\u0026D Insurance \n Voluntary Life \u0026 AD\u0026D insurance \n Paid vacation plus paid holidays \n Retirement plan with employer match \n Paid parental leave \n Wellness Programs \n \n Annual Base Salary Range \n $200,000 — $350,000 USD","salary_min":200000,"salary_max":350000,"location":"New York, NY","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["pytorch","tensorflow","search","deep-learning","reinforcement-learning","robotics","research"],"apply_url":"https://www.jumptrading.com/hr/job?gh_jid=8122860","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-18T18:54:10Z","expires_at":"2026-09-29T13:47:31.484855Z","created_at":"2026-08-27T13:48:12.175153Z","updated_at":"2026-08-30T13:47:31.614748Z","company_name":"Jump Trading","company_slug":"jump-trading","company_logo_url":"https://www.google.com/s2/favicons?domain=jumptrading.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/d5aae1c9-cae9-47d5-9fce-478d44a8cb20"},{"id":"a27af2e3-0c1e-45a0-8d92-2485073e28ff","company_id":"12105b3e-eb1d-4a92-95b6-855042facaf1","title":"Applied Scientist II","slug":"applied-scientist-ii-c3c7e3aa","description":"At Qualtrics, we create software the world’s best brands use to deliver exceptional frontline experiences, build high-performing teams, and design products people love. But we are more than a platform—we are the creators and stewards of the Experience Management category serving over 18K clients globally. Building a category takes grit, determination, and a disdain for convention—but most of all it requires close-knit, high-functioning teams with an unwavering dedication to serving our customers. When you join one of our teams, you’ll be part of a nimble group that’s empowered to set aggressive goals and move fast to achieve them. Strategic risks are encouraged and complex problems are solved together, by passing the mic and iterating until the best solution comes to light. You won’t have to look to find growth opportunities—ready or not, they’ll find you. From retail to government to healthcare, we’re on a mission to bring humanity, connection, and empathy back to business. Join over 5,000 people across the globe who think that’s work worth doing.\n Applied Scientist II \n  \n Why We Have This Role \n We are looking for talented and innovative Applied Scientist to bring our Core AI Machine Learning and Artificial Intelligence R\u0026D and strategy to the next level. Our goal is to personalize the Qualtrics experience using ML and AI features showcasing Qualtrics data as a core value proposition and competitive advantage.\n As an Applied Scientist at Qualtrics, you should love building cutting-edge predictive models to solve hard customer problems. Crafting models in an agile environment to withstand hyper growth and owning quality from end-to-end is a rewarding challenge and one of the reasons Qualtrics is such an exciting place to work!\n How You’ll Find Success \n \n Leverage your deep knowledge of artificial intelligence (AI) principles, including machine learning, natural language processing, computer vision, and reinforcement learning.\n Use your understanding of both supervised and unsupervised learning techniques, and their applications in building intelligent systems.\n Develop and optimize algorithms for building scalable and efficient GenAI applications.\n Tackle challenging problems in creative ways, leveraging generative models to address real-world use cases and drive innovation.\n Use effective communication skills to articulate technical concepts to non-technical stakeholders and gather requirements for GenAI application development.\n Show strong programming skills in languages like Python, along with proficiency in deep learning frameworks such as TensorFlow, PyTorch, or similar.\n \n How You’ll Grow \n \n Passion for leveraging cutting-edge AI technology to create innovative GenAI applications that have a meaningful impact on businesses, industries, and society.\n Commitment to developing GenAI applications that adhere to ethical standards and promote positive societal impact while minimizing potential risks.\n Drive to push the boundaries of what's possible with AI, and to contribute to the advancement of the field through research, experimentation, and collaboration.\n Willingness to stay updated with the latest advancements in AI research and technology, and to continuously learn and adapt to new methodologies and best practices.\n Agility to pivot and iterate on GenAI applications based on feedback, emerging trends, and changing business requirements.\n \n Things You’ll Do \n \n Address challenges in products through Large Language Models, Deep Learning and Data Science approaches and publish research papers.\n Work as part of a multidisciplinary team to research, implement, evaluate, optimize, productize and maintain cutting-edge machine learning models to meet the demands of our rapidly growing business\n Stay on top of the latest developments in machine learning and related research, and present research findings with the broader community\n Work closely with, and incorporate feedback from other specialists, engineers, and product managers\n Lead and engage in design reviews, modeling discussions, requirement definitions and other technical activities in diverse capacity\n Contribute to and inspire the Conversational AI, NLP, and Data Science technology roadmap at Qualtrics.\n Design, build, and evaluate Agentic AI systems to solve complex customer challenges.\n \n What We’re Looking For On Your Resume \n \n Bachelors and Ph.D in Computer Science or related fields\n Solid understanding of machine learning fundamentals and tool ecosystem\n 3+ years of combined academic and industrial research experience in machine learning, NLP, information retrieval, deep learning or a related field.\n Experience with Agentic AI systems, including design, development, and rigorous evaluation of agent performance.\n Deep learning implementation expertise (TensorFlow, PyTorch etc)\n Excellent command of at least one modern programming language (preferably Python)\n Deep understanding of machine learning model life cy","salary_min":155000,"salary_max":203500,"location":"Reston, VA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"mid","tags":["llm","search","tensorflow","deep-learning","fine-tuning","agents","computer-vision","healthcare"],"apply_url":"https://www.qualtrics.com/careers/us/en/job/8115079?gh_jid=8115079","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-17T18:10:13Z","expires_at":"2026-09-29T13:49:13.405443Z","created_at":"2026-08-25T18:33:38.583782Z","updated_at":"2026-08-30T13:49:13.540253Z","company_name":"Qualtrics","company_slug":"qualtrics","company_logo_url":"https://www.google.com/s2/favicons?domain=qualtrics.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/a27af2e3-0c1e-45a0-8d92-2485073e28ff"},{"id":"09cc77a9-0b4b-49a6-ada6-446f95619ce6","company_id":"a0000000-0000-0000-0000-000000000009","title":"Member of Technical Staff, Multilingual","slug":"member-of-technical-staff-multilingual-c99001f8","description":"Who are we?\n\nCohere is the leading security-first enterprise AI company.  We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems.\n\nWe’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that.\n\nWe obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft.\n\nWe are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us!\n\n\n\nRole Overview:\n\nWe are looking for world-class research scientists and research engineers to build cutting-edge multilingual AI to serve the world!\n\nAt Cohere, we care deeply about building technologies that are broadly accessible and useful, regardless of language.  As a Member of Technical Staff on the Multilingual team, you'll be at the forefront of advancing language models that serve the world. You’ll push the boundaries of what's possible in natural language processing. This isn't just a technical role—it's an opportunity to contribute to groundbreaking research that will define the next generation of AI.\n\n\n\n\nKey Responsibilities:\n\n - Lead the design and implementation of scalable solutions to improve multilingual LLM performance across a wide variety of skills – if we need it to make a great multilingual model, you can build it!\n\n - Is perfect for someone passionate about languages and AI, with a keen eye for detail and strong technical skills.\n\n - Offers the opportunity to contribute to cutting-edge language technology, making a global impact.\n\n - Requires a self-starter who can work independently and deliver results efficiently.\n\n - Publish research findings and contribute to academic discourse in top-tier venues\n\n - Mentor junior team members and shape global best practices in multilingual AI / NLP\n   \n   \n\nQualifications:\n\n - PhD in Computer Science, Linguistics, or related field (or equivalent experience)\n\n - Proven track record in large-scale data processing and ML pipeline development\n\n - Expert in Python and software engineering best practice\n\n - Deep understanding of multilingual data challenges and NLP fundamentals\n\n - Passion for advancing language technology and making global impact\n\n - Strong publication record or demonstrated research potential\n\n - Excellent communicator able to bridge technical and research communities\n   \n   \n\nNote:\n\n - This role can be based remotely or from one of our office locations listed on the job description - there is no minimum in-office qualification requirement. We care most about hiring exceptional people regardless of locations, though please check the location listed on the posting for guidance around the core time zone or working hours alignment expected for the role.\n\n - If some of the above doesn’t line up perfectly with your experience, we still encourage you to apply.\n\n\nWe value and celebrate diversity and strive to create an inclusive work environment for all. We welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations request form and we will work together to meet your needs.\n\n\n\n\nCompensation:\n\nCohere is committed to fair and transparent pay practices. The salary range listed for this role reflects the expected base compensation. Actual compensation offered will be determined by factors such as location, level, job-related knowledge, skills, education, and experience.\n\n - For candidates in the US, the Compensation Range is: $110,000 - $370,000 [USD]\n\n - For candidates in Canada, the Compensation Range is: $165,000 - $460,000 [CAD]\n\n\n\n\n\n\nFULL-TIME EMPLOYEES AT COHERE ENJOY THESE PERKS:\n\n - A weekly lunch stipend of $75/£75 or equivalent in your local currency for lunch.\n\n - Full health and dental benefits, including a separate budget for mental health.\n\n - RRSP matching, 401K, Pension Scheme.\n\n - 100% Parental Leave top-up for up to 6 months, for either parent.\n\n - Annual enrichment benefits:\n   \n   Arts \u0026 culture, fitness/wellness, quality time, and a workspace improvement credit.\n   \n   Education \u0026 learning stipend for conferences, courses, and coaching.\n\n - 6 weeks of paid vacation (30 working days!)\n\n - Budget for traveling to other offices if you are remote, plus an annual company offsite.\n\n\n\n\nHOW AND WHERE WE WORK:\n\n - Cohere is remote-friendly, but we also have offices in Toronto, London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul with more opening soon.\n\n - For those in the office: a daily lunch program, plenty of snacks, and re","salary_min":165000,"salary_max":460000,"location":"London, UK","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"lead","tags":["payments","llm","nlp","search"],"apply_url":"https://jobs.ashbyhq.com/cohere/a87be947-00f0-4a4c-a690-a4922f88f553/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-17T15:57:25.097Z","expires_at":"2026-09-29T13:31:52.3333Z","created_at":"2026-06-28T14:01:33.291718Z","updated_at":"2026-08-30T13:31:52.473166Z","company_name":"Cohere","company_slug":"cohere","company_logo_url":"https://www.google.com/s2/favicons?domain=cohere.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/09cc77a9-0b4b-49a6-ada6-446f95619ce6"},{"id":"c329b188-7612-4641-ac6d-796f7502eb3d","company_id":"c587b06c-b6f0-4d1d-b694-6fb6abc2a6bb","title":"Senior Research Engineer, LLM Training \u0026 Post-Training","slug":"senior-research-engineer-llm-training-post-training-b05c8bf9","description":"Who We Are \n Lightning AI is the company behind PyTorch Lightning. Founded in 2019, we build an end-to-end platform for developing, training, and deploying AI systems—designed to take ideas from research to production with less friction.\n Through our merger with Voltage Park, a neocloud and AI Factory, Lightning AI combines developer-first software with cost-efficient, large-scale compute. Teams get the tools they need for experimentation, training, and production inference, with security, observability, and control built in.\n We serve solo researchers, startups, and large enterprises. Lightning AI operates globally with offices in New York City, San Francisco, Seattle, and London, and is backed by Coatue, Index Ventures, Bain Capital Ventures, and Firstminute.\n The Way We Work\n The people who thrive here are builders who move fast, communicate openly, take ownership, and continuously improve themselves, their teams, and our company. Here's what that looks like in practice:\n \n Move with Urgency: We move quickly, make thoughtful decisions, and keep momentum. We value action over perfection and learn by shipping.\n Take Ownership: We own outcomes, not just our individual work. We make decisions that move the company forward and follow through.\n Communicate Openly: We communicate directly, seek to understand, and create clarity for others. Honest conversations help us move faster together.\n Build Great Teams: We lead by example, empower others, and create healthy teams where people can do their best work.\n Raise the Bar: We're always improving ourselves. We learn from feedback, consistently challenge ourselves to grow, and focus on the work that matters most.\n Think Long-Term: We design for what's next. We create scalable systems, simplify complexity, and use AI and automation to amplify our impact.\n \n  \n What We're Looking For\n We're are looking for an experienced Senior Research Engineer who has built, trained, and optimized modern transformer-based language models to join our Research Engineering function at Lightning.\n This role will focus on advancing how large language models are trained, fine-tuned, evaluated, and deployed across Lightning AI's platform and real-world customer workloads. It will work across model training, post-training, PyTorch, distributed systems, and AI systems engineering to improve model quality, training efficiency, and developer productivity while collaborating closely with researchers, infrastructure engineers, and customers.\n We're looking for someone who enjoys turning cutting-edge research into production systems. You have deep experience training and improving transformer-based language models, strong software engineering fundamentals, and a passion for solving difficult problems across model training, evaluation, and AI systems. Rather than building applications on top of existing models, you're motivated by improving the models themselves and the systems that power them. Our work spans models that power the Lightning AI platform, customer-specific model workloads, and research that translates into reusable training and platform capabilities.\n This role is hybrid with a minimum of 2 in-office days per week in San Francisco, Seattle, NYC, or London, with fully remote work considered for candidates outside of our office hub locations. All employees participate in occasional team and company offsites. \n  \n What You'll Do \n \n Design, build, and optimize training and post-training pipelines for large language models.\n Improve model quality through supervised fine-tuning, continued pretraining, preference optimization, reinforcement learning, evaluation, and experimentation.\n Build and improve PyTorch-based training infrastructure, tooling, and developer workflows.\n Optimize distributed training across multi-GPU environments by improving throughput, memory efficiency, scalability, and GPU utilization.\n Investigate model training issues, including convergence, instability, communication overhead, and performance bottlenecks.\n Design evaluation methodologies, benchmark models, analyze failure modes, and acheive model improvements through experimentation.\n Collaborate directly with customers to understand real-world workloads and translate those learnings into improvements across Lightning AI's research platform.\n Partner closely with research, infrastructure, and platform engineering teams to build production-ready AI systems.\n Contribute to open-source projects through new features, tooling improvements, documentation, and community engagement\n \n  \n What You’ll Need \n Required Qualifications \n \n Significant experience training, fine-tuning, evaluating, and/or optimizing transformer-based language models using PyTorch.\n Experience with modern LLM training and post-training techniques such as continued pretraining, SFT, RLHF, preference optimization (DPO, PPO, GRPO), reward modeling, or similar approaches.\n Strong understanding of distributed training and multi-node systems, ","salary_min":165000,"salary_max":310000,"location":"New York, NY","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["pre-training","search","fine-tuning","gpu","distributed-systems","llm","reinforcement-learning","pytorch"],"apply_url":"https://job-boards.greenhouse.io/lightningai/jobs/7860628003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-12T19:54:51Z","expires_at":"2026-09-29T13:33:58.434095Z","created_at":"2026-08-25T18:27:03.622997Z","updated_at":"2026-08-30T13:33:58.572247Z","company_name":"Lightning AI","company_slug":"lightning-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=lightning.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/c329b188-7612-4641-ac6d-796f7502eb3d"},{"id":"8833a326-2222-405d-9178-38d091a4acf2","company_id":"40ad6923-4b5c-44e5-b30c-340bb35deab6","title":"Senior Machine Learning Engineer ","slug":"senior-machine-learning-engineer-5107596c","description":"About Us:\n\nHere at Ambience, we never set out to be just another scribe. We’re building the AI intelligence platform that restores humanity to healthcare and drives meaningful ROI for health systems across the country.\n\nOur technology helps providers focus on delivering great care by removing the administrative burden that pulls them away from patients and away from their most impactful work. Ambience delivers real-time coding-aware documentation and clinical workflow support across ambulatory, emergency and inpatient settings at the top health systems in North America.\n\nOur teams operate relentlessly with extreme ownership to build the best solutions for our health system partners. We value candor, positivity and deep thought — and we expect a lot from each other because we know the problems we’re solving truly matter.\n\nAmbience was ranked #1 for Improving the Clinician Experience in the KLAS Research Emerging Solutions Top 20 Report, recognized by Fast Company as one of the Next Big Things in Tech, named one of the best AI companies in healthcare by Inc., and selected as a LinkedIn Top Startup in 2024 and 2025. We’re backed by Oak HC/FT, Andreessen Horowitz (a16z), OpenAI Startup Fund, and Kleiner Perkins — and we’re just getting started.\n\n\n\n\nTHE ROLE:\n\nAs a Senior Machine Learning Engineer at Ambience, you will build and improve the AI systems that power our clinical products. You’ll own complex projects end-to-end, from diagnosing production failures and designing evaluations to building, deploying, and iterating on model and agentic systems.\nThis is a highly hands-on role with significant technical ownership. You’ll work closely with clinicians, product managers, and fellow engineers to translate cutting-edge research into reliable, production-grade AI systems.\n\nOur engineering roles are hybrid — working onsite at our San Francisco office three days per week.\n\n\n\n\nWHAT YOU’LL DO:\n\n - Build Trustworthy AI Evaluation Systems: Design and own evaluation pipelines for LLM and agentic systems, combining automated graders, regression testing, production feedback, and human evaluation to measure real product quality.\n\n - Improve Production Model Behavior: Diagnose high-impact failure modes and test improvements across prompting, retrieval, context, routing, data, fine-tuning, or other model and system interventions.\n\n - Build Agentic AI Systems: Develop production systems involving tool use, retrieval, context and state management, routing, orchestration, tracing, and failure recovery.\n\n - Build Data and Improvement Flywheels: Turn production failures and user feedback into better datasets, evaluations, and model behavior through active learning and systematic iteration.\n\n - Stay at the Cutting Edge: Distill insights from recent research in LLMs, agents, NLP, speech, and multimodal AI and translate promising ideas into practical experiments.\n\n - Own AI Systems End-to-End: Work across models, data, evaluation, orchestration, serving, and observability, while remaining deeply hands-on in code and production debugging.\n\n\n\n\nWHO YOU ARE: \n\n -  Strong Production AI Experience\n   5+ years in production ML, research engineering, or applied AI.\n   Have built a consequential production AI system or materially improved model behavior in production.\n   Strong understanding of modern LLMs, transformers, and production AI systems.\n\n - Deep Evaluation Experience\n   Experienced designing evaluations for LLMs, agents, or other complex AI systems.\n   Can turn ambiguous quality problems into measurable dimensions, datasets, and experiments.\n   Familiar with challenges such as grader bias, leakage, misleading aggregate metrics, regression detection, and offline-online mismatch.\n\n - Agentic Systems Experience\n   Experience building production systems involving multiple models, tools, retrieval, context, state, routing, or orchestration.\n   Understands reliability and failure modes in complex AI workflows, not just individual model calls.\n\n - Production-Grade Software Engineer\n   Proficient in Python and modern ML frameworks; PyTorch preferred.\n   Comfortable with deployment, observability, CI/CD, and containerized systems.\n   Still highly hands-on: writes code, inspects traces, analyzes failures, and debugs production systems.\n\n - Data-Centric AI Developer\n   Skilled at building high-quality datasets and feedback loops.\n   Experienced using production failures, user feedback, and active learning to improve model and system quality.\n\n - Effective Interdisciplinary Collaborator\n   Able to work closely with clinicians, product managers, and fellow engineers.\n   Strong communicator who can simplify complex AI concepts for diverse audiences.\n   Comfortable owning ambiguous technical problems and driving them to measurable outcomes.\n   \n   \n   Nice-to-Haves\n\n - Experience with realtime voice, conversational AI, or multimodal systems.\n\n - Experience with fine-tuning, post-training, or model adaptation.\n\n - Prior work i","salary_min":225000,"salary_max":300000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["pytorch","generative-ai","payments","llm","agents","search","healthcare","nlp"],"apply_url":"https://jobs.ashbyhq.com/ambiencehealthcare/6f44370e-1979-4237-a3db-94c46ec7ec9c/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-11T16:31:17.016Z","expires_at":"2026-09-29T13:37:47.545943Z","created_at":"2026-08-25T18:28:24.330364Z","updated_at":"2026-08-30T13:37:47.678173Z","company_name":"Ambience Healthcare","company_slug":"ambience-healthcare","company_logo_url":"https://www.google.com/s2/favicons?domain=ambience.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/8833a326-2222-405d-9178-38d091a4acf2"},{"id":"991f8296-af04-49ea-b15e-7d9b5fc51b29","company_id":"a0000000-0000-0000-0000-000000000001","title":"AI Infrastructure Operations, Demand Planning","slug":"ai-infrastructure-operations-demand-planning-3cf6fb61","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 runs one of the largest and fastest-growing infrastructure fleets in the industry, across multiple accelerator families, CPU families, clouds, neoclouds, and on-prem sites. Capacity Engineering owns the data, tooling, and systems that let Anthropic plan, measure, and maximize utilization of that fleet: we partner on supply deals, wire telemetry from day zero, own the canonical capacity data layer, and build the planning and enforcement tools every research and product team relies on. This role sits in the Planning pillar, on the Demand Planning team, and works daily with research engineering, pretraining, inference, compute supply, finance, and external vendors.\n You own the tranches. The job has two halves that feed each other. Upstream, you take the Demand Planning forecast and turn it into per-tranche requirements — shape, interconnect, region, supporting resources, date — and carry those into sourcing negotiations and data center build reviews so we contract for capacity we can actually use when we need it. Downstream, you own the integrated schedule and system of record for every tranche in flight — from contracted through reserved, ingested, in-cluster, healthy, and occupied — and you drive the owners of each hop to their dates. Every slip you see downstream becomes a contract-language fix, an automation, or a correction fed back to the forecast.\n What you'll do\n \n Turn the forecast into per-tranche requirements. Take the Demand Planning forecast plus direct input from research, pretraining, and inference planners, and convert it into concrete accelerator, interconnect, region, supporting-resource, and date requirements for each tranche. Represent those in sourcing negotiations and data center build reviews, including which contractual terms actually move delivery dates.\n Qualify tranches for deliverability before signature. The Capacity Planner signs fit-to-forecast; you sign whether the shape can land schedulable, healthy, and instrumented in that region on that date, with storage, egress, identity in place.\n Close the delivery loop. Track forecast-versus-delivered on shape, region, and timing for every tranche; publish the variance; and feed it back to Demand Planning and into the next contract.\n Own the bring-up system of record. Define the canonical contract-to-occupied state machine with explicit entry and exit criteria per stage, and make it a first-class object in the capacity data layer so every downstream tool sees in-flight capacity, not only what has landed.\n Run a portfolio of bring-ups in parallel — new cloud regions, on-prem sites, neocloud blocks — with one integrated schedule spanning provider milestones, cluster creation, network turn-up, storage readiness, health burn-in, and first-workload landing. \n Drive readiness automation: All capacity systems are fully integrated for all new capacity, from contracted through ingested, automated and scaled.\n Instrument and publish the numbers that matter — time-to-occupied and paid-idle dollars per tranche — with executive-level reporting on status, tradeoffs, and risk across the portfolio.\n \n What you bring\n \n Significant experience delivering large-scale infrastructure — cloud regions, accelerator clusters, HPC systems, or bare-metal fleets — at multi-region scale or ≥10k accelerators (or CPU/storage equivalent).\n Technical range from through cluster orchestration and node health, up to the telemetry and planning tables on top — enough to debug where they disagree rather than route it.\n SQL and enough Python to answer your own questions and build your own reporting.\n A degree in a technical field or an equivalent engineering track record.\n \n Preferred\n \n Reserved-capacity onboarding, private offers, or capacity commitments with cloud or neocloud providers.\n Enough demand-planning exposure to challenge a forecast, translate it into per-tranche requirements, and feed delivery variance back into it.\n Data center or colocation delivery: power and space planning, network turn-up, site acceptance, vendor management.\n Accelerator health and burn-in, collective-communications sanity testing, or fleet-health SLOs — and a rigorous definition of \"healthy.\"\n Systems of record or lifecycle services for infrastructure assets.\n Onboarding a new hardware generation into an existing scheduler and observability stack.\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","salary_min":320000,"salary_max":405000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["alignment","pre-training","search","infrastructure"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5382750008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-11T12:14:01Z","expires_at":"2026-09-29T13:30:10.669189Z","created_at":"2026-08-25T18:26:10.627699Z","updated_at":"2026-08-30T13:30:10.854518Z","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/991f8296-af04-49ea-b15e-7d9b5fc51b29"},{"id":"f89646a2-035d-42e4-a101-17b62b4dbb93","company_id":"d8e15a46-b80d-4228-8e7b-34f00357f377","title":"RVP - AI Natives","slug":"rvp-ai-natives-aaf7116d","description":"Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale — unleashing the potential of businesses and people. The Elastic Search AI Platform, used by more than 50% of the Fortune 500, brings together the precision of search and the intelligence of AI to enable everyone to accelerate the results that matter. By taking advantage of all structured and unstructured data — securing and protecting private information more effectively — Elastic’s complete, cloud-based solutions for search, security, and observability help organizations deliver on the promise of AI.\n What Is The Role: \n \n Elastic is seeking a visionary RVP - AI Natives to lead, scale, and accelerate the growth of our specialized AI Native business unit. In this strategic executive role, you will lead a high-performing team responsible for a dual motion: selling Elastic’s enterprise Search, vector search, and generative AI platform directly to digital-native AI companies (e.g., OpenAI, Anthropic, Perplexity, Cohere), and co-building/co-selling strategic sell-with partnerships with these ecosystems. If you are an accomplished software sales leader with a proven track record of scaling high-performing enterprise teams, navigating complex partner ecosystems, and possessing a deep passion for AI infrastructure, we want you to shape the future of AI Search with us. \n \n Location: West of Texas \n What You Will Be Doing: \n \n \n Lead and Coach Teams: Manage, mentor, and coach a team of specialized Account Executives and Strategic Alliance Managers to successfully sell into AI-native organizations and execute high-impact sell-with motions. Actively foster an environment of teamwork, transparency, creativity, and continuous improvement while traveling regularly to provide hands-on executive support. \n \n Drive GTM \u0026 Sell-With Strategy: Build, execute, and own a dual-track GTM strategy: direct enterprise expansion into AI-native companies and strategic sell-with co-sell motions with major AI foundation model providers and platforms. Formulate account coverage, incentive alignment, pipeline generation programs, and customer segmentation tailored to this hyper-growth market segment. \n \n Quota Achievement \u0026 Operational Excellence: Consistently meet or exceed revenue and co-sell targets. Lead by example using rigorous pipeline management, structured forecasting, and deal execution frameworks. Travel regularly to engage directly in deal structuring, co-sell strategy, and executive closures. \n \n Engage C-Suite and Partner Ecosystems: Actively engage in strategic deal cycles to establish trusted-advisor relationships at the CxO, Founder, and VP level within top-tier AI companies. Build strategic, long-term alliances with partner management across product, business development, and go-to-market teams. \n \n Cross-Functional Collaboration: Partner closely with Elastic's global executive leadership , Product, Engineering, Marketing, and Solutions Architecture teams. Provide real-time market telemetry from leading AI innovators to influence Elastic's AI roadmap , while ensuring seamless joint PoC executions and strategic co-marketing initiatives. \n \n What You Bring: \n \n \n SaaS \u0026 Enterprise Sales Leadership : Proven track record leading high-performing software sales teams, with a documented history of quota overachievement in dynamic, high-growth, or strategic alliance environments. \n \n AI \u0026 Search Domain Fluency: Deep understanding of the modern AI tech stack, including vector databases, hybrid search infrastructure, LLMs, retrieval-augmented generation (RAG), and generative AI platforms. \n \n Ecosystem \u0026 Sell-With Expertise: Demonstrated experience building strategic co-sell or sell-with partnerships alongside major cloud, AI, or SaaS ecosystem leaders. \n \n Operational Discipline: Solid analytical capability and data-driven decision-making. Expertise in applying structured methodologies (e.g., MEDDPICC) to complex direct and joint sales cycles. \n \n Entrepreneurial Mindset: The resilience , agility, and initiative-taking attitude required to pioneer a hyper-growth, rapidly evolving vertical and build new market playbooks from scratch. \n \n Bonus Points: \n \n \n Prior experience selling directly to or partnering closely with premier AI foundation model companies (e.g., Anthropic, OpenAI, Perplexity). \n \n Familiarity with open-source software, developer-centric infrastructure GTM strategies, and product-led growth (PLG) dynamics. \n \n #LI-AM2 \n  \n Compensation for this role is in the form of base salary plus a variable component, that together comprise the On-Target Earnings (OTE).   On-Target Earnings (OTE) are based on a 60/40 pay mix (base salary / target variable).   \n The typical starting OTE range for new hires in this role is listed below.  This range represents the lowest to highest OTE we reasonably and in good faith believe we would pay for this role at the time of this posting.  We may ul","salary_min":283200,"salary_max":447900,"location":"United States","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["embeddings","rag","llm","search","generative-ai"],"apply_url":"https://jobs.elastic.co/jobs?gh_jid=8114222\u0026gh_jid=8114222","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-07T15:21:22Z","expires_at":"2026-09-29T13:39:15.215658Z","created_at":"2026-08-25T18:29:01.987196Z","updated_at":"2026-08-30T13:39:15.356245Z","company_name":"Elastic","company_slug":"elastic","company_logo_url":"https://www.google.com/s2/favicons?domain=www.elastic.co\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/f89646a2-035d-42e4-a101-17b62b4dbb93"}],"page":1,"per_page":20,"total":679,"total_is_exact":true,"total_pages":34}