{"access":{"advertiser_pricing_url":"https://aidevboard.com/pricing","catalog_url":"https://aidevboard.com/api/v1/catalog","description":"Public read endpoints are open and free. API keys are optional for stable agent identity and keyed hourly throttling.","docs_url":"https://aidevboard.com/docs","mode":"open","register_url":"https://aidevboard.com/api/v1/register"},"degraded":false,"estimated":false,"has_next":true,"jobs":[{"id":"48720738-0f4b-483d-9739-14039ae457d0","company_id":"a0000000-0000-0000-0000-000000000001","title":"Research Engineer, Performance RL (Reinforcement Learning) ","slug":"research-engineer-performance-rl-2f0da25a","description":"About Anthropic \n Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n About the RL Teams \n Our Reinforcement Learning teams lead Anthropic's reinforcement learning research and development, playing a critical role in advancing our AI systems. We've contributed to all Claude models, with significant impacts on the autonomy and coding capabilities of Claude Sonnet 4.6 and Opus 4.6. Our work spans several key areas:\n \n \n Developing systems that enable models to use computers effectively\n \n Advancing code generation through reinforcement learning\n \n Pioneering fundamental RL research for large language models\n \n Building scalable RL infrastructure and training methodologies\n \n Enhancing model reasoning capabilities\n \n We collaborate closely with Anthropic's alignment and frontier red teams to ensure our systems are both capable and safe. We partner with the applied production training team to bring research innovations into deployed models, and are dedicated to implement our research at scale. Our Reinforcement Learning teams sit at the intersection of cutting-edge research and engineering excellence, with a deep commitment to building high-quality, scalable systems that push the boundaries of what AI can accomplish.\n About the Role \n We're hiring for the Code RL team within the RL organization. As a Research Engineer, you'll advance our models' ability to safely write correct, fast code for accelerators.\n You'll need to know accelerator performance well to turn it into tasks and signals models can learn from. Specifically, you will:\n \n \n Invent, design and implement RL environments and evaluations.\n \n Conduct experiments and shape our research roadmap.\n \n Deliver your work into training runs.\n \n Collaborate with other researchers, engineers, and performance engineering specialists across and outside Anthropic.\n \n You may be a good fit if you:\n \n \n Have expertise with accelerators (CUDA, ROCm, Triton, Pallas), ML framework programming (JAX or PyTorch).\n \n Have worked across the stack – kernels, model code, distributed systems.\n \n Know how to balance research exploration with engineering implementation.\n \n Are passionate about AI's potential and committed to developing safe and beneficial systems.\n \n Strong candidates may also have:\n \n \n Experience with reinforcement learning.\n \n Experience porting ML workloads between different types of accelerators.\n \n Familiarity with LLM training methodologies.\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 $350,000 — $850,000 USD \n Logistics \n Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\n Required field of study:  A field relevant to the role as demonstrated through coursework, training, or professional experience\n Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position\n Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.\n Visa sponsorship:  We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.\n We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a ","salary_min":350000,"salary_max":850000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["reinforcement-learning","code-generation","search","pytorch","llm","jax","fine-tuning","gpu"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5160330008","is_featured":true,"is_sticky":true,"status":"active","published_at":"2026-03-23T16:27:59Z","expires_at":"2026-08-15T14:00:29.666185Z","created_at":"2026-04-13T09:36:00.086246Z","updated_at":"2026-07-16T14:00:29.796553Z","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/48720738-0f4b-483d-9739-14039ae457d0"},{"id":"f8c6c621-b459-40f6-b41d-0baa191734ff","company_id":"a0000000-0000-0000-0000-000000000001","title":"Research Lead, Training Insights","slug":"research-lead-training-insights-6091f430","description":"About Anthropic \n Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n About the role \n As a Research Lead on the Training Insights team, you'll develop the strategy for, and lead execution on, how we measure and characterize model capabilities across training and deployment. This is a hands-on leadership role: you'll drive original research into new evaluation methodologies while leading a small team of researchers and research engineers doing the same.\n Your work will span the full lifecycle of model development. You'll research and build new long-horizon evaluations that test the boundaries of what our models can achieve, develop novel approaches to measuring emerging capabilities, and deepen our understanding of how those capabilities develop — both during production RL training and after. You'll also take a cross-organizational view, working across Reinforcement Learning, Pretraining, Inference, Product, Alignment, Safeguards, and other teams to map the landscape of model evaluations at Anthropic and identify critical gaps in coverage.\n This role carries significant visibility and impact. You'll help shape the evaluation narrative for model releases, contributing directly to how Anthropic communicates about its models to both internal and external audiences. Done well, you will change how the industry measures and understands model capabilities, significantly furthering our safety mission.  \n Responsibilities:  \n \n Build new novel and long-horizon evaluations\n Develop novel measurement approaches for understanding how model capabilities emerge and evolve during RL training\n Lead strategic evaluation coverage across the company\n Shape the evaluation narrative for model releases\n Lead and mentor a small team of researchers and research engineers, setting research direction and fostering a culture of rigorous, creative research\n Design evaluation frameworks that balance scientific rigor with the practical demands of production training schedules\n Build and maintain relationships across Anthropic's research organization to ensure evaluation insights inform training and deployment decisions\n Contribute to the broader research community through publications, open-source contributions, or external engagement on evaluation best practices\n \n You may be a good fit if you:  \n \n Have significant experience designing and running evaluations for large language models or similar complex ML systems\n Have led technical projects or teams, either formally or through sustained ownership of critical research directions\n Are equally comfortable designing experiments and writing code—you can move between research and implementation fluidly\n Think strategically about what to measure and why, not just how to measure it\n Can synthesize information across multiple teams and workstreams to form a coherent picture of model capabilities\n Communicate complex technical findings clearly to both technical and non-technical audiences\n Are results-oriented and thrive in fast-paced environments where priorities shift based on research findings\n Care deeply about AI safety and want your work to directly influence how capable AI systems are developed and deployed\n \n Strong candidates may also have:  \n \n Experience building evaluations for long-horizon or agentic tasks\n Deep familiarity with Reinforcement Learning training dynamics and how model behavior changes during training\n Published research in machine learning evaluation, benchmarking, or related areas\n Experience with safety evaluation frameworks and red teaming methodologies\n Background in psychometrics, experimental psychology, or other measurement-focused disciplines\n A track record of communicating evaluation results to inform high-stakes decisions about model development or deployment\n Experience managing or mentoring researchers and engineers\n \n Representative projects:  \n \n Designing and implementing a suite of long-horizon evaluations that test model capabilities on tasks requiring sustained reasoning, planning, and tool use over extended interactions\n Building systems to track capability development across RL training checkpoints, surfacing insights about when and how specific capabilities emerge\n Conducting a cross-org audit of evaluation coverage, identifying blind spots, and prioritizing new evaluations to fill critical gaps across Pretraining, RL, Inference, and Product\n Developing the evaluation methodology and narrative for a major model release, working with research leads and communications to clearly characterize model capabilities and limitations\n Researching and prototyping novel evaluation approaches for capabilities that are difficult to measure with existing benchmarks\n Leading a team","salary_min":850000,"salary_max":850000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["alignment","llm","pre-training","search","agents","reinforcement-learning","research"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5139654008","is_featured":true,"is_sticky":false,"status":"active","published_at":"2026-03-06T17:15:29Z","expires_at":"2026-08-15T14:00:31.404308Z","created_at":"2026-04-13T09:36:01.625992Z","updated_at":"2026-07-16T14:00:31.517772Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/f8c6c621-b459-40f6-b41d-0baa191734ff"},{"id":"9cf703e4-28cb-47a7-9151-d26f9745f43d","company_id":"74257563-5513-4a8d-a0f7-01f00c59aed6","title":"Senior Machine Learning Engineer, Relevance and Personalization (Query Intelligence)","slug":"senior-machine-learning-engineer-relevance-and-personalization-query-intelligence-b6fdeb9a","description":"Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way. \n The Community You Will Join: \n The Relevance and Personalization team at Airbnb is responsible for search and recommendation across the entire Airbnb digital platform. In this role you'll focus on query intelligence, the front door of search working on critical, impactful projects that turn what a guest types, taps, or says into a precise understanding of their intent, spanning autocomplete and smart compose, query tagging, query expansion, and intent modeling across Stays, Experiences, and Services.\n The Difference You Will Make: \n Query understanding is where every search begins, and it directly shapes retrieval, ranking, and ultimately the perfect match between guests and hosts. We build cutting-edge AI technologies across the end-to-end search ranking product stack w.r.t. data pipelines, feature and model innovations, serving and experimentation efficiency, leveraging rich signals from various types of data (structured, sequential, image, text, etc) and increasingly large language models at Airbnb. You'll build the models that parse free-form and natural-language multimodal queries, extract entities and location context, classify intent, and anticipate what guests want before they finish typing. We collaborate closely with teams across Airbnb to develop the ranking solutions and support a healthy marketplace for hosts and guests to further Airbnb's mission of creating a world where people can Belong Anywhere. Some past publications from the team can be found here: https://sites.google.com/view/airbnb-relevance-publications/home \n A Typical Day:  \n \n Work with large scale structured and unstructured data, build and continuously improve cutting edge Machine Learning models for Airbnb product, business and operational use cases, with a focus on query understanding.\n Develop query understanding capabilities — autocomplete and smart compose, query tagging (sequence tagging / NER), query expansion, and query/user intent modeling — and natural-language (\"search in your own words\") search experiences powered by modern NLP and LLMs.\n Work collaboratively with cross-functional partners including software engineers, product managers, operations and data scientists, identify opportunities for business impact, understand, refine, and prioritize requirements for machine learning models, drive engineering decisions, and quantify impact.\n Hands-on develop, productionize, and operate Machine Learning models and pipelines at scale, including both batch and real-time use cases.\n Leverage third-party and in-house Machine Learning tools \u0026 infrastructure to develop reusable, highly differentiating and high-performing Machine Learning systems, enable fast model development, low-latency serving and ease of model quality upkeep.\n Example projects include: smart compose and language generation for search, LLM-based sequence taggers, LLM-driven query/location expansion, intent classification, and user-intent sequence modeling.\n \n Your Expertise: \n \n 5+ years of industry experience in applied Machine Learning, inclusive MS or PhD in relevant fields.\n Strong programming (Scala / Python / Java / C++ or equivalent) and data engineering skills.\n Deep understanding of Machine Learning best practices (eg. training/serving skew minimization, A/B test, feature engineering, feature/model selection), algorithms (eg. neural networks/deep learning, optimization) and domains (eg. natural language processing, personalization, search and recommendation, marketplace optimization).\n Experience with 3 or more of these technologies: Tensorflow, PyTorch, Kubernetes, Spark, Airflow (or equivalent), Kafka (or equivalent), data warehouse (eg. Hive).\n Industry experience building end-to-end Machine Learning models.\n Experience applying large language models and modern NLP — e.g., sequence tagging/NER, text generation, intent classification, or embedding/representation learning.\n Familiarity with building natural-language, AI-native and agentic search experiences is a plus.\n Exposure to architectural patterns of large, high-scale software applications (e.g., well-designed APIs, high volume data pipelines, efficient algorithms, models).\n \n Your Location: \n This position is US - Remote Eligible. The role may include occasional work at an Airbnb office or attendance at offsites, as agreed to with your manager. While the position is Remote Eligible, you must live in a state where Airbnb, Inc. has a registered entity. Click here for the up-to-date list of excluded states. This list is continuously evolving, so please check back with us if the state you live in is on the exclusion list. If your po","salary_min":200000,"salary_max":235000,"location":"United States","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["tensorflow","agents","search","data-pipeline","deep-learning","generative-ai","llm","pytorch"],"apply_url":"https://careers.airbnb.com/positions/8065789?gh_jid=8065789","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-14T23:54:51Z","expires_at":"2026-08-15T14:10:04.208902Z","created_at":"2026-07-15T14:11:23.002744Z","updated_at":"2026-07-16T14:10:04.325662Z","company_name":"Airbnb","company_slug":"airbnb","company_logo_url":"https://www.google.com/s2/favicons?domain=airbnb.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/9cf703e4-28cb-47a7-9151-d26f9745f43d"},{"id":"1a206bd4-e5b5-4a4d-8384-65e3e9c3f4ec","company_id":"2721f049-2cf2-4e3e-82d0-8d8df89c8f90","title":"SDR, Tavily","slug":"sdr-tavily-497ebf81","description":"About Nebius: \n Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure.\n Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI.\n Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R\u0026D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R\u0026D.\n \n About Tavily \n We’re building the search engine for AI agents. Our API is designed from the ground up to power RAG and real-time reasoning in AI systems. By connecting LLMs to high quality, trustworthy web content, we help developers build agents that are not only intelligent, but also informed.\n We work with some of the most innovative teams in AI, from small startups shaping the ecosystem to the largest enterprises deploying AI at scale. Whether it’s powering sales assistants, research copilots, or internal knowledge tools, we’re the missing link between LLMs and the real world \n The role \n We’re hiring to expand on the immediate success and impact our founding SDR team has had. You will be the engine behind the engine, helping convert high-volume developer inbound into qualified opportunities and building an outbound motion to the teams creating cutting edge agents and AI products.\n Your responsibilities will include:   \n \n Promptly follow up with inbound and outbound prospects via email, LinkedIn, and calls to ensure no lead slips through the cracks.\n Build a deep understanding of Tavily’s ICP: AI engineers, data science teams, and product leaders building agentic systems to identify where they need grounded, real-time search in their product\n Qualify opportunities and book meetings for the Sales team, ensuring they are equiped with the correct information to win the deal.\n Provide structured feedback on signals, workflows, and outputs to help us improve Tavily based on real-life testing.\n \n We expect you to have:   \n \n 1–2 years of Sales, SDR, Analytics or Computer Science experience in a SaaS or tech environment preferred (open to exceptional entry-level candidates).\n You are in Austin and excited about an in-person office environment (think 4 days per week).\n You're resilient, energized by building relationships, and genuinely excited to learn.\n You’re genuinely curious about AI and enjoy learning what new products and teams are building, even if you’re not technical yourself.\n Ability to balance high-volume outreach with thoughtful experimentation and feedback.\n \n Key employee benefits in the US: \n \n Health insurance:  100% company-paid medical, dental, and vision coverage for employees and families.\n 401(k) plan:  Up to 4% company match with immediate vesting.\n Parental leave:  20 weeks paid for primary caregivers, 12 weeks for secondary caregivers.\n Remote work reimbursement:  Up to $85/month for mobile and internet.\n Disability \u0026 life insurance : Company-paid short-term, long-term and life insurance coverage.\n \n \n Pay Transparency \n We offer competitive compensation and benefits packages. Actual compensation will be determined based on job-related factors, including experience, skills, qualifications, the level at which the candidate is hired, and geographic location, consistent with applicable law.\n Base Compensation Range\n $71,700 — $89,600 USD \n Benefits \u0026 Perks: \n \n Competitive compensation\n Career growth and learning opportunities\n Flexibility and ownership\n Collaborative and innovative culture\n Opportunity to work on impactful AI projects\n International environment and talented teams\n \n What's it like to work at Nebius: \n Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI \n Equal Opportunity Statement: \n Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law.\n Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. \n If you need accommodations during the application process, please let us know.","salary_min":71700,"salary_max":89600,"location":"Austin, TX","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["code-generation","agents","cloud","llm","rag","search"],"apply_url":"https://careers.nebius.com/?gh_jid=4927819101","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-14T20:05:03Z","expires_at":"2026-08-15T14:15:47.944545Z","created_at":"2026-07-15T14:17:14.584259Z","updated_at":"2026-07-16T14:15:48.089969Z","company_name":"Nebius","company_slug":"nebius","company_logo_url":"https://www.google.com/s2/favicons?domain=nebius.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/1a206bd4-e5b5-4a4d-8384-65e3e9c3f4ec"},{"id":"53523380-38ba-4300-ab1a-a7402a41ff8f","company_id":"a0000000-0000-0000-0000-000000000001","title":"Staff+ Software Engineer, Capacity Engineering","slug":"staff-software-engineer-capacity-engineering-751b8b8f","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 manages one of the largest and fastest-growing infrastructure fleets in the industry — spanning multiple accelerator families, cpu families and clouds. The Capacity Engineering team is responsible for making sure all our infrastructure resources are accounted for, well-utilized, and efficiently allocated. We own the data, tooling, and operational systems that let Anthropic plan, measure, and maximize utilization across first-party and third-party compute.\n As an engineer on Capacity Engineering, you will build the production systems that power this work: data pipelines that ingest and normalize telemetry from heterogeneous cloud environments, observability tooling that gives the org real-time visibility into fleet health, and performance instrumentation that measures how efficiently every major workload uses the hardware it’s running on. You will be expected to write production-quality code every day, operate alongside Kubernetes-native infrastructure at meaningful scale, and directly influence decisions around one of Anthropic’s largest areas of spend.\n You’ll collaborate closely with research engineering, infrastructure, inference, and finance teams. The work requires someone who can move between data engineering, systems engineering, and observability with comfort — and who thrives in a high-autonomy, high-ambiguity environment.\n This is a pipeline role feeding four areas. Depending on your background and business priority, you’ll focus primarily in one, but the boundaries are fluid and the problems overlap: \n \n Data platform Pipelines that ingest occupancy and utilization telemetry from Kubernetes clusters, normalize billing and usage across cloud providers, and serve the BigQuery tables the rest of the org queries against. Correctness, completeness, and latency are the job, not a footnote. Consumers range from research engineers to finance to leadership, so it's product work as much as engineering: defining schema contracts, making data discoverable, and figuring out what people actually need.\n Planning Knowing what the fleet has, where it's going, and what's in the way. Making the state of the fleet legible and actionable in real time: cluster health tooling, capacity planning platforms, alerting on occupancy drops and allocation problems, and systemic fixes to scheduling and fragmentation. Kubernetes operations on one side, cross-team coordination on the other.\n Efficiency Measuring and improving how effectively every major workload uses the hardware it runs on. Instrumenting utilization across training, inference, and eval systems, building benchmarking infrastructure, establishing per-config baselines, and working directly with system-owning teams to close the gaps. The metric has to be good enough that the team on the hook for it agrees with the number.\n Attribution and forecasting Connecting what the fleet costs to what the business is doing with it. Reconciling CSP billing exports against vendor telemetry and internal systems with mismatched schemas, attributing spend to the workloads and teams that generate it, and turning inference demand signals and research roadmaps into a defensible compute plan. Efficiency metrics have to survive contact with finance: stripped of pure demand and unit-price effects, reproducible month over month, and legible to a CFO.\n \n Key responsibilities \n \n Build the planning and allocation stack — the tools leadership uses to allocate capacity, teams use to plan against their allocations, and the scheduler enforces. Cross-region and cross-provider placement, guardrails, queueing, occupancy KPIs.\n Drive the efficiency programs: stranding and rightsizing, unused capacity recovery, and job-level utilization across training, inference, and eval. Establish per-config baselines and work with system-owning teams to close the gaps. At this fleet size a single point of utilization is worth eight figures a month.\n Own attribution and forecasting — reconcile billing across ten-plus providers against telemetry and internal systems, attribute spend to the workloads that generate it, and turn demand signals and research roadmaps into a defensible compute plan and supply pipeline.\n Build the data platform underneath all of it: pipelines ingesting occupancy, utilization, and cost from a rapidly diversifying fleet into BigQuery, with real ownership of completeness, latency SLOs, and gap detection. Every new provider is a net-new integration.\n Operate Kubernetes-native systems at scale — collection agents, workload labeling, and the taint/reservation/scheduling behavior that determines what capacity is ac","salary_min":320000,"salary_max":485000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["search","data-pipeline","payments","infrastructure"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5310731008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-14T19:16:22Z","expires_at":"2026-08-15T14:00:39.165919Z","created_at":"2026-07-15T14:00:36.496615Z","updated_at":"2026-07-16T14:00:39.319483Z","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/53523380-38ba-4300-ab1a-a7402a41ff8f"},{"id":"7befba03-6985-475e-9441-9bd1ccb173d8","company_id":"a0000000-0000-0000-0000-000000000001","title":"Research Engineer, Chip Design RL (Reinforcement Learning)","slug":"research-engineer-chip-design-rl-reinforcement-learning-39e9d4d0","description":"About Anthropic \n Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n About the RL teams \n Our Reinforcement Learning teams lead Anthropic's reinforcement learning research and development, playing a critical role in advancing our AI systems. We've contributed to all Claude models, with significant impacts on the autonomy and coding capabilities of Claude Fable 5 and Opus 4.8. Our work spans several key areas:\n \n Developing systems that enable models to use computers effectively\n Advancing code generation through reinforcement learning\n Pioneering fundamental RL research for large language models\n Building scalable RL infrastructure and training methodologies\n Enhancing model reasoning capabilities\n \n We collaborate closely with Anthropic's alignment and frontier red teams to ensure our systems are both capable and safe. We partner with the applied production training team to bring research innovations into deployed models, and are dedicated to implement our research at scale. Our Reinforcement Learning teams sit at the intersection of cutting-edge research and engineering excellence, with a deep commitment to building high-quality, scalable systems that push the boundaries of what AI can accomplish.\n About the role \n We're hiring for the Code RL team within the RL organization. As a Research Engineer, you'll advance our models' ability to design silicon. Hardware design is difficult and unforgiving – exactly the sort of domain we want Claude to excel at.\n You'll leverage your chip design expertise and turn it into tasks and signals for models to learn from. Specifically, you will: \n \n Invent, design, and implement RL environments and evaluations for agentic RTL generation, design (including formal) verification, physical design optimization.\n Work on cross-cutting RL considerations such as EDA-tool latency optimization and proxy rewards.\n Conduct experiments and shape our roadmap.\n Deliver your work into research and production training runs.\n Collaborate with other researchers and engineers across and outside Anthropic.\n \n You may be a good fit if you: \n \n Have expertise in ASIC or FPGA design: RTL, design verification (UVM, formal methods, coverage-driven), physical design (synthesis, place-and-route, timing closure), PPA optimization, DFT, ECOs.\n Are fluent with industry EDA tools and processes.\n Have taped out chips and have experience going from spec to silicon.\n Know how to balance research exploration with engineering implementation.\n Are passionate about AI's potential and committed to developing safe and beneficial systems.\n \n Strong candidates may also have: \n \n Experience with reinforcement learning, evaluations or environments.\n Built tooling or automation around chip design flows.\n Worked on ML accelerators or high-performance compute hardware.\n Familiarity with high-level synthesis or architecture simulators.\n The annual compensation range for this role is listed below. \n For sales roles, the range provided is the role’s On Target Earnings (\"OTE\") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\n Annual Salary:\n $500,000 — $850,000 USD \n Logistics \n Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\n Required field of study:  A field relevant to the role as demonstrated through coursework, training, or professional experience\n Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position\n Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.\n Visa sponsorship:  We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.\n We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To","salary_min":500000,"salary_max":850000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["fine-tuning","reinforcement-learning","agents","search","alignment","llm","code-generation","research"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5231612008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-13T22:19:12Z","expires_at":"2026-08-15T14:00:28.138626Z","created_at":"2026-07-15T14:00:27.407964Z","updated_at":"2026-07-16T14:00:28.258454Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/7befba03-6985-475e-9441-9bd1ccb173d8"},{"id":"e57cf48d-3756-4016-8e50-400a76bbaa5d","company_id":"714f360f-a244-487d-b3f0-0c43518a9e66","title":"Staff Machine Learning Engineer, Computer Vision","slug":"staff-machine-learning-engineer-computer-vision-147d8a7f","description":"About Pinterest: \n Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.\n Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the  flexibility to do your best work. Creating a career you love? It’s Possible.\n At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI.\n Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here .\n Within Pinterest, the Pinterest Labs organization focuses on applied ML research and development. Labs works across a broad variety of AI/ML initiatives—including core computer vision, multimodal representation learning, heterogeneous graph neural networks, generative modeling, and recommender systems. This is the group that develops the foundation ML models that fully leverage the tens of billions of Pins and the associated knowledge graph to improve the core product.\n We are currently hiring for the Visual Modeling team in Labs, which develops Pinterest's in-house visual encoder. In this role, you'll work with Pinterest's rich visual-text dataset to train large-scale models from scratch that are continuously shipped to production to power visualization features. You'll build multimodal representations that power applications such as recommender systems, Semantic IDs, and a range of downstream ML models. The visual encoder also produces visual tokens that power our in-house VLM and composed image retrieval models. The core visual pod is a small group (~10 engineers) inside Labs, which allows for deep collaboration. For example, engineers working on multimodal representation also contribute to our internal text-to-image generation Canvas project—collaborating on autoencoder design or on reward function development for RL training.\n  \n What you’ll do: \n \n Prototype state-of-the-art visual encoders that power Pinterest's recommender systems and internal visual language models.\n Experiment with billion-scale datasets and gain hands-on experience with large-scale GPU computing.\n Build flexible visual reasoning tools such as composed image retrieval, promptable detection/segmentation, and instruction-tuned embedding and generative models.\n Read research papers, participate in group discussions, and help brainstorm the company's overall visual generative strategy.\n Help collect relevant visual instruction training data that can be shared across multimodal representation, composed image retrieval, text-to-image generation and visual language modeling.\n Publish and share your work through conferences, paper submissions, and blog posts.\n Mentor junior researchers and research interns within the Pinterest Labs organization.\n  \n \n What we’re looking for: \n \n Research engineers and scientists with experience building and training computer vision models.\n Experience with multimodal representations and visual language modeling is strongly preferred.\n A track record of research contributions (e.g., publications, open-source work) and/or shipping ML models to production.\n Hands-on experience with large-scale model training and modern deep learning frameworks (e.g., PyTorch).\n Strong collaboration skills and a demonstrated ability to work effectively in a small, fast-moving team.\n M.S. or PhD in Machine Learning or related academic areas, or equivalent work experience.\n Publications at top ML conferences\n Experience using Cursor, Copilot, Codex, or similar AI coding assistants for development, debugging, testing, and refactoring\n \n  \n Relocation Statement: \n \n This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.\n \n  \n In-Office Requirement Statement: \n \n We let the type of work you do guide the collaboration style. That means we're not always working in an office, but we continue to gather for key moments of collaboration and connection.\n This role will need to be in the office for in-person collaboration 1-2 times/quarter and therefore can be situated anywhere in the country.\n \n  \n #LI-REMOTE #LI-AK7\n At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The posit","salary_min":189308,"salary_max":389753,"location":"San Francisco, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"lead","tags":["search","deep-learning","generative-ai","code-generation","pytorch","computer-vision","machine-learning"],"apply_url":"https://www.pinterestcareers.com/jobs/?gh_jid=8015537","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-13T17:51:37Z","expires_at":"2026-08-15T14:09:24.364141Z","created_at":"2026-07-15T14:10:33.975738Z","updated_at":"2026-07-16T14:09:24.488319Z","company_name":"Pinterest","company_slug":"pinterest","company_logo_url":"https://www.google.com/s2/favicons?domain=www.pinterest.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e57cf48d-3756-4016-8e50-400a76bbaa5d"},{"id":"f5536ef4-fbd2-4708-bd41-546593698786","company_id":"52f44519-9f93-4eac-ae0b-8be13e385ebe","title":"Research Engineer","slug":"research-engineer-evals-22a46522","description":"RESEARCH ENGINEER\n\n\n\nYou'll build the evaluation systems that tell us whether Firecrawl actually works. That sounds simple. It isn't. Our core promise, convert any URL into clean, structured, LLM-ready data reliably, is hard to measure rigorously across millions of different websites, formats, and edge cases. As the systems we're measuring get more complex, the question \"did that work?\" gets harder, not easier.\n\nThis isn't an eval role where you inherit a framework and run benchmarks. You'll design the metrics, build the pipelines, generate the datasets, and own the feedback loop from output quality back to model and product decisions. If you care about what \"good\" actually means and have the engineering depth to measure it, this is the role.\n\n\n\nSalary Range: $210,000–$275,000/year (Range shown is for U.S.-based employees in San Francisco, CA. Compensation outside the U.S. is adjusted fairly based on your country's cost of living.)\n\nEquity Range: Competitive equity — details shared during the process.\n\nLocation: San Francisco, CA (Hybrid, on-site required)\n\nJob Type: Full-Time\n\nExperience: 4+ years in ML, research engineering, or data-heavy backend, with real evaluation work\n\nVisa: Must 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 147k+ GitHub stars, and developers, agents, and category-defining AI companies build on us every day. Growth like this is rare, and we're just getting started.\n\nWe're a small team punching far above our weight. Everyone here owns a real piece of the product and company, end to end, and runs it themselves - no hiding behind process or headcount.\n\nThis is a place for people who want to work at the frontier: an AI company building the infrastructure other AI companies run on, not one bolting AI onto an existing product. We move fast, go deep, and are building the tools superintelligence will rely on to gather data from the web.\n\n\n\n\nWHAT YOU'LL DO\n\n - Design the metrics that define what \"good output\" actually means across millions of sites, formats, and edge cases\n\n - Build the pipelines and harnesses that measure quality rigorously and at scale\n\n - Generate and curate the datasets that make evaluation trustworthy\n\n - Own the feedback loop from output quality back to model and product decisions\n\n - Turn \"did that work?\" into an answer the whole team can act on\n\n\n\n\nWHAT WE'RE LOOKING FOR\n\n - You have the engineering depth to build real evaluation systems, not just run existing ones\n\n - You care deeply about what \"good\" means and how to measure it rigorously\n\n - You're comfortable owning ambiguous problems where the metric itself has to be invented\n\n - You move fast and close the loop - you'd rather ship, measure, and iterate than perfect on paper\n\n\n\n\nWHAT WE'RE NOT LOOKING FOR\n\n - Someone who only wants to run benchmarks someone else designed\n\n - A pure researcher who won't build the systems, or a pure engineer who won't think about methodology\n\n - Someone who needs a fully-specced ticket to start\n\n\n\n\nA NOTE ON PACE\n\nWe operate at an absurd level of urgency because the window for what we're building won't stay open forever. If that excites you, keep reading. If it doesn't, no hard feelings — but this role probably isn't for you.\n\n\n\n\nBENEFITS \u0026 PERKS\n\n\n\n\nAVAILABLE TO ALL EMPLOYEES\n\n - Salary that makes sense — $210,000-$275,000/year (U.S.-based), based on impact, not tenure\n\n - Own a piece — Gain competitive equity in what you're helping build\n\n - Generous PTO — 15 days mandatory, anything after 24 days, just ask (holidays excluded); take the time you need to recharge\n\n - Parental leave — 12 weeks fully paid, for all parents\n\n - Wellness stipend — $100/month for the gym, therapy, massages, or whatever keeps you human\n\n - Learning \u0026 Development — Expense up to $1,000/year toward anything that helps you grow professionally\n\n - Team offsites — A change of scenery, minus the trust falls\n\n - Sabbatical — 3 paid months off after 4 years, do something fun and new\n\n\n\n\nAVAILABLE TO US-BASED FULL-TIME EMPLOYEES\n\n - Full coverage, no red tape — Medical, dental, and vision (100% for employees, 50% for spouse/kids) — no weird loopholes, just care that works\n\n - Life \u0026 Disability insurance — Employer-paid short-term disability, long-term disability, and life insurance — coverage for life's curveballs\n\n - Supplemental options — Optional accident, critical illness, hospital indemnity, and voluntary life insurance for extra peace of mind\n\n - Doctegrity telehealth — Talk to a doctor from your couch\n\n - 401(k) plan — Retirement might be a ways off, but ","salary_min":210000,"salary_max":275000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["llm","search","agents","research"],"apply_url":"https://jobs.ashbyhq.com/firecrawl/25092c0e-9a32-4191-af79-050738213704/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-12T23:24:01.87Z","expires_at":"2026-08-15T14:16:24.433714Z","created_at":"2026-05-14T14:16:13.169544Z","updated_at":"2026-07-16T14:16:24.553511Z","company_name":"Firecrawl","company_slug":"firecrawl","company_logo_url":"https://www.google.com/s2/favicons?domain=firecrawl.dev\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/f5536ef4-fbd2-4708-bd41-546593698786"},{"id":"a817e307-049e-4a51-a9c1-ec5a47864c6e","company_id":"52f44519-9f93-4eac-ae0b-8be13e385ebe","title":"Search Engineer","slug":"search-engineer-fe5d3ce2","description":"SEARCH ENGINEER\n\n\n\nYou'll build the systems that let anyone turn the open web into a search index. Firecrawl's search product is one of our fastest-growing surfaces, and we need engineers who can make crawling, ranking, and retrieval fast, reliable, and cheap at scale. You'll own real infrastructure from day one — not tickets in a backlog.\n\n\n\nSalary Range: $190,000-$260,000/year (Range shown is for U.S.-based employees in San Francisco, CA. Compensation outside the U.S. is adjusted fairly based on your country's cost of living.)\n\nEquity Range: Competitive equity — details shared during the process.\n\nLocation: San Francisco, CA (Hybrid, on-site required)\n\nJob Type: Full-Time\n\nExperience: 3+ years building production backend or infra systems\n\nVisa: Must 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 147k+ GitHub stars, and developers, agents, and category-defining AI companies build on us every day. Growth like this is rare, and we're just getting started.\n\nWe're a small team punching far above our weight. Everyone here owns a real piece of the product and company, end to end, and runs it themselves - no hiding behind process or headcount.\n\nThis is a place for people who want to work at the frontier: an AI company building the infrastructure other AI companies run on, not one bolting AI onto an existing product. We move fast, go deep, and are building the tools superintelligence will rely on to gather data from the web.\n\n\n\n\nWHAT YOU'LL DO\n\n - Design and build the crawling, indexing, and retrieval systems behind Firecrawl Search\n\n - Push down latency and cost per query while search volume grows\n\n - Improve ranking quality and freshness for LLM-driven retrieval\n\n - Own services end to end — design, ship, monitor, and iterate in production\n\n - Work directly with the Head of Search and the rest of the search team on the roadmap\n\n\n\n\nWHAT WE'RE LOOKING FOR\n\n - You've built and operated backend or distributed systems at real scale\n\n - You care about latency, cost, and correctness in equal measure\n\n - You're comfortable owning ambiguous problems and turning them into shipped systems\n\n - You move fast and close the loop — you'd rather ship, measure, and iterate than perfect on paper\n\n\n\n\nWHAT WE'RE NOT LOOKING FOR\n\n - Someone who needs a fully-specced ticket to start\n\n - Someone who wants to specialize narrowly and hand off everything else\n\n - Someone who optimizes for process over shipping\n\n\n\n\nA NOTE ON PACE\n\nWe operate at an absurd level of urgency because the window for what we're building won't stay open forever. If that excites you, keep reading. If it doesn't, no hard feelings — but this role probably isn't for you.\n\n\n\n\nBENEFITS \u0026 PERKS\n\n\n\n\nAVAILABLE TO ALL EMPLOYEES\n\n - Salary that makes sense — $190,000–$260,000/year, based on impact, not tenure\n\n - Own a piece — Gain competitive equity in what you're helping build\n\n - Generous PTO — 15 days mandatory, anything after 24 days, just ask (holidays excluded); take the time you need to recharge\n\n - Parental leave — 12 weeks fully paid, for all parents\n\n - Wellness stipend — $100/month for the gym, therapy, massages, or whatever keeps you human\n\n - Learning \u0026 Development — Expense up to $1,000/year toward anything that helps you grow professionally\n\n - Team offsites — A change of scenery, minus the trust falls\n\n - Sabbatical — 3 paid months off after 4 years, do something fun and new\n\n\n\n\nAVAILABLE TO US-BASED FULL-TIME EMPLOYEES\n\n - Full coverage, no red tape — Medical, dental, and vision (100% for employees, 50% for spouse/kids) — no weird loopholes, just care that works\n\n - Life \u0026 Disability insurance — Employer-paid short-term disability, long-term disability, and life insurance — coverage for life's curveballs\n\n - Supplemental options — Optional accident, critical illness, hospital indemnity, and voluntary life insurance for extra peace of mind\n\n - Doctegrity telehealth — Talk to a doctor from your couch\n\n - 401(k) plan — Retirement might be a ways off, but future-you will thank you\n\n - Pre-tax benefits — Access to FSAs and commuter benefits (US-only) to help your wallet out a bit\n\n - Pet insurance — Because fur babies are family too\n\n\n\n\nAVAILABLE TO SF-BASED EMPLOYEES\n\n - SF HQ perks — Snacks, drinks, team lunches, intense ping pong, and peak startup energy\n\n - E-Bike transportation — A loaner electric bike to get you around the city, on us\n\n\n\n\nINTERVIEW PROCESS\n\nApplication Review — Send us your work and a quick note on why this excites you. Show us what you've built — search systems, indexing","salary_min":190000,"salary_max":260000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["agents","llm","search","distributed-systems"],"apply_url":"https://jobs.ashbyhq.com/firecrawl/762b4426-b4aa-4377-96d3-51f40c59cbf7/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-12T21:39:54.338Z","expires_at":"2026-08-15T14:16:24.960548Z","created_at":"2026-07-15T14:17:50.19743Z","updated_at":"2026-07-16T14:16:25.089271Z","company_name":"Firecrawl","company_slug":"firecrawl","company_logo_url":"https://www.google.com/s2/favicons?domain=firecrawl.dev\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/a817e307-049e-4a51-a9c1-ec5a47864c6e"},{"id":"52c0d743-42dc-4708-98d3-2eda7148a5c5","company_id":"66e863fb-9aaf-40df-996c-eb439e6f857e","title":"Software Engineer","slug":"software-engineer-b563a434","description":"About Glean: \n  \n Glean is the Work AI platform that helps everyone work smarter with AI. What began as the industry’s most advanced enterprise search has evolved into a full-scale Work AI ecosystem, powering intelligent Search, an AI Assistant, and scalable AI agents on one secure, open platform. With over 100 enterprise SaaS connectors, flexible LLM choice, and robust APIs, Glean gives organizations the infrastructure to govern, scale, and customize AI across their entire business - without vendor lock-in or costly implementation cycles. \n  \n At its core, Glean is redefining how enterprises find, use, and act on knowledge. Its Enterprise Graph and Personal Knowledge Graph map the relationships between people, content, and activity, delivering deeply personalized, context-aware responses for every employee. This foundation powers Glean’s agentic capabilities - AI agents that automate real work across teams by accessing the industry’s broadest range of data: enterprise and world, structured and unstructured, historical and real-time. The result: measurable business impact through faster onboarding, hours of productivity gained each week, and smarter, safer decisions at every level. \n  \n Recognized by Fast Company as one of the World’s Most Innovative Companies (Top 10, 2025), by CNBC’s Disruptor 50, Bloomberg’s AI Startups to Watch (2026), Forbes AI 50, and Gartner’s Tech Innovators in Agentic AI, Glean continues to accelerate its global impact. With customers across 50+ industries and 1,000+ employees in more than 25 countries, we’re helping the world’s largest organizations make every employee AI-fluent, and turning the superintelligent enterprise from concept into reality. \n  \n If you’re excited to shape how the world works, you’ll help build systems used daily across Microsoft Teams, Zoom, ServiceNow, Zendesk, GitHub, and many more - deeply embedded where people get things done. You’ll ship agentic capabilities on an open, extensible stack, with the craft and care required for enterprise trust, as we bring Work AI to every employee, in every company. \n About the Role\n Glean Technologies, Inc. has multiple positions available for a Software Engineer. As a Software Engineer, you will help build a software-based platform that can scale indefinitely, including scalable enterprise search solutions. You will work across distributed systems, data pipelines, APIs, and user interfaces to deliver secure, high-quality products that meet customer needs.\n What You Will Do\n \n Develop a software-based platform that can scale indefinitely, including scalable enterprise search solutions.\n Build large-scale fault-tolerant distributed systems, preferably with knowledge of performance benchmarking tools and performance tuning on Linux-based systems.\n Perform thorough code review for peers, including interface design, code quality, and testing strategies.\n Understand customer requirements and implement them in solutions.\n Work closely with the company’s product teams to understand customer requirements and ensure features satisfy those requirements and are delivered effectively with high quality.\n Implement data ingestion pipelines to retrieve data from enterprise data sources and build a secure search index over that data.\n Design and implement user interfaces used by enterprise workers to search enterprise content.\n Design APIs to build other search-based applications.\n \n Who You Are\n \n You have a Master’s degree, or foreign degree equivalent, in Computer Science, Engineering (any field), or a related quantitative discipline, plus three (3) months of experience in the job offered or in any occupation in a related field.\n You have experience working on infrastructure for distributed systems or cloud-native applications, or experience building full-stack applications that span front-end, REST APIs, and application server, or experience training and productionizing machine learning, or information retrieval systems.\n You have experience with Go or C++.\n You have experience with Java.\n You have experience with Python.\n You have experience with TypeScript.\n You have algorithmic design skills.\n You have experience with data analytics.\n You have experience with Node.\n You have experience with Ruby on Rails, Django, or Flask.\n You have experience with React.\n Any suitable combination of education, training, and/or experience is acceptable.\n \n  \n Location: Mountain View, CA. Telecommuting is an option.\n Compensation \u0026 Benefits\n The standard base salary range for this position is $187,741 - $234,000 annually. Compensation offered will be determined by factors such as location, level, job-related knowledge, skills, and experience. Certain roles may be eligible for variable compensation, equity, and benefits.\n  \n We offer a comprehensive benefits package including competitive compensation, Medical, Vision, and Dental coverage, generous time-off policy, and the opportunity to contribute to your 401k ","salary_min":187741,"salary_max":234000,"location":"Mountain View, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["distributed-systems","search","agents","data-pipeline","api-design","cloud","llm"],"apply_url":"https://job-boards.greenhouse.io/gleanwork/jobs/4713145005","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-10T18:21:38Z","expires_at":"2026-08-15T14:04:04.297114Z","created_at":"2026-07-12T14:03:17.754756Z","updated_at":"2026-07-16T14:04:04.428691Z","company_name":"Glean","company_slug":"glean","company_logo_url":"https://www.google.com/s2/favicons?domain=glean.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/52c0d743-42dc-4708-98d3-2eda7148a5c5"},{"id":"08e650fb-0032-430e-b5d5-a6c3007cb351","company_id":"66e863fb-9aaf-40df-996c-eb439e6f857e","title":"Software Engineer","slug":"software-engineer-4d15fbe9","description":"About Glean: \n  \n Glean is the Work AI platform that helps everyone work smarter with AI. What began as the industry’s most advanced enterprise search has evolved into a full-scale Work AI ecosystem, powering intelligent Search, an AI Assistant, and scalable AI agents on one secure, open platform. With over 100 enterprise SaaS connectors, flexible LLM choice, and robust APIs, Glean gives organizations the infrastructure to govern, scale, and customize AI across their entire business - without vendor lock-in or costly implementation cycles. \n  \n At its core, Glean is redefining how enterprises find, use, and act on knowledge. Its Enterprise Graph and Personal Knowledge Graph map the relationships between people, content, and activity, delivering deeply personalized, context-aware responses for every employee. This foundation powers Glean’s agentic capabilities - AI agents that automate real work across teams by accessing the industry’s broadest range of data: enterprise and world, structured and unstructured, historical and real-time. The result: measurable business impact through faster onboarding, hours of productivity gained each week, and smarter, safer decisions at every level. \n  \n Recognized by Fast Company as one of the World’s Most Innovative Companies (Top 10, 2025), by CNBC’s Disruptor 50, Bloomberg’s AI Startups to Watch (2026), Forbes AI 50, and Gartner’s Tech Innovators in Agentic AI, Glean continues to accelerate its global impact. With customers across 50+ industries and 1,000+ employees in more than 25 countries, we’re helping the world’s largest organizations make every employee AI-fluent, and turning the superintelligent enterprise from concept into reality. \n  \n If you’re excited to shape how the world works, you’ll help build systems used daily across Microsoft Teams, Zoom, ServiceNow, Zendesk, GitHub, and many more - deeply embedded where people get things done. You’ll ship agentic capabilities on an open, extensible stack, with the craft and care required for enterprise trust, as we bring Work AI to every employee, in every company. \n About the Role\n Glean Technologies, Inc. has multiple positions available for a Software Engineer. As a Software Engineer, you will help build a software-based platform that can scale indefinitely, including scalable enterprise search solutions. You will work across distributed systems, data pipelines, APIs, and user interfaces to deliver secure, high-quality products that meet customer needs.\n What You Will Do\n \n Develop a software-based platform that can scale indefinitely, including scalable enterprise search solutions.\n Build large-scale fault-tolerant distributed systems, preferably with knowledge of performance benchmarking tools and performance tuning on Linux-based systems.\n Perform thorough code review for peers, including interface design, code quality, and testing strategies.\n Understand customer requirements and implement them in solutions.\n Work closely with the company’s product teams to understand customer requirements and ensure features satisfy those requirements and are delivered effectively with high quality.\n Implement data ingestion pipelines to retrieve data from enterprise data sources and build a secure search index over that data.\n Design and implement user interfaces used by enterprise workers to search enterprise content.\n Design APIs to build other search-based applications.\n \n Who You Are\n \n You have a Bachelor's degree, or foreign degree equivalent, in Computer Science, Engineering (any field), or a related quantitative discipline, and six (6) months of experience in the job offered or in any occupation in related field.\n You have experience working on infrastructure for distributed systems or cloud-native applications, or experience building full-stack applications that span front-end, REST APIs, and application server, or experience training and productionizing machine learning, or information retrieval systems.\n You have experience with Go or C++.\n You have experience with Java.\n You have experience with Python.\n You have experience with TypeScript.\n You have algorithmic design skills.\n You have experience with data analytics.\n You have experience with Node.\n You have experience with Ruby on Rails, Django, or Flask.\n You have experience with React.\n Any suitable combination of education, training, and/or experience is acceptable.\n \n  \n Location: Mountain View, CA. Telecommuting is an option.\n Compensation \u0026 Benefits\n The standard base salary range for this position is $215,000 - $278,900 annually. Compensation offered will be determined by factors such as location, level, job-related knowledge, skills, and experience. Certain roles may be eligible for variable compensation, equity, and benefits.\n  \n We offer a comprehensive benefits package including competitive compensation, Medical, Vision, and Dental coverage, generous time-off policy, and the opportunity to contribute to your 401k plan","salary_min":215000,"salary_max":278900,"location":"Mountain View, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["agents","llm","search","api-design","cloud","data-pipeline","distributed-systems"],"apply_url":"https://job-boards.greenhouse.io/gleanwork/jobs/4713977005","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-10T18:21:37Z","expires_at":"2026-08-15T14:04:04.206223Z","created_at":"2026-07-12T14:03:17.835612Z","updated_at":"2026-07-16T14:04:04.327025Z","company_name":"Glean","company_slug":"glean","company_logo_url":"https://www.google.com/s2/favicons?domain=glean.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/08e650fb-0032-430e-b5d5-a6c3007cb351"},{"id":"537b089a-1139-46c6-9166-2dc6b9693a2f","company_id":"3da82454-107f-427f-88e7-01f315ef93fb","title":"Research Engineer - RL Infrastructure ","slug":"research-engineer-rl-infrastructure-af69c92c","description":"OWN YOUR INTELLIGENCE\n\n\n\nPrime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team.\n\n\n\nOur platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale - from SFT and RL to tool use, agent workflows, and continuously improving production models. We are building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research, and the full-stack platform our own research team uses to build them. The next generation of AI companies, enterprises, and research teams do not just need more GPUs. They need the ability to turn their own workflows, tools, data, and feedback loops into superintelligence they own.\n\nWe train open frontier models and ship the same stack to our customers. Its spans the full stack of training, deploying and continuously improving models — compute, large-scale RL, environments, sandboxes, evals, and deployment.\n\n\n\nPrime Intellect has raised $150M in total funding from Founders Fund, Radical Ventures, NVIDIA, and exceptional AI, infrastructure, and enterprise operators — including Andrej Karpathy, Dwarkesh Patel, and leaders and founders from Ramp, Perplexity, Harvey, Mercor, Zapier, Datadog, Cognition, OpenAI, Thinking Machines, Together AI, SemiAnalysis, LangChain, Browserbase, Cloudflare, Sierra, Databricks, Airbnb, OpenRouter, Standard Intelligence, Fleet, Core Auto, and more. We are looking for people who want to build at the intersection of frontier research, real infrastructure, and go-to-market for a category that does not fully exist yet.\n\n\n\n\n\nWHAT YOU’LL WORK ON\n\n - Build and optimize the systems infrastructure behind large-scale RL and distributed training workloads by contributing to our prime-rl https://github.com/PrimeIntellect-ai/prime-rl framework.\n\n - Improve end-to-end training efficiency across compute, memory, networking, and scheduling layers.\n\n - Design and implement low-level performance optimizations, including kernels, communication paths, and runtime improvements.\n\n - Work on distributed training systems spanning data, tensor, and pipeline parallel workloads.\n\n - Help shape the architecture of our RL training stack, including async rollout and post-training systems.\n\n - Contribute to open-source libraries and internal infrastructure used for frontier-scale model training.\n\n - Collaborate closely with researchers and infrastructure engineers to translate bottlenecks into concrete systems improvements.\n\n - Stay at the frontier of training systems, inference systems, compiler/runtime tooling, and hardware-aware optimization techniques.\n\n\n\n\n\nYOU MAY BE A FIT IF YOU HAVE\n\n - Strong systems engineering experience in AI/ML infrastructure, especially around large-scale model training or inference.\n\n - Deep familiarity with PyTorch and distributed training frameworks such as PyTorch Distributed, DeepSpeed, FSDP, Megatron, vLLM, Ray, or related tooling.\n\n - Experience optimizing training performance across kernels, memory movement, communication overhead, or parallelization strategy.\n\n - Hands-on experience with large-scale training techniques including data parallelism, tensor parallelism, and pipeline parallelism.\n\n - Strong understanding of GPU architecture, profiling, and performance debugging.\n\n - Ability to identify bottlenecks across the stack and drive improvements from first principles.\n\n - Comfort working in a fast-moving environment with ambiguous problems and high ownership.\n\n\n\n\nESPECIALLY EXCITING\n\n - Experience writing or optimizing CUDA / Triton kernels.\n\n - Experience with compiler or runtime optimization for ML systems.\n\n - Experience working on RL training infrastructure, rollout systems, or asynchronous training pipelines.\n\n - Experience with multi-node GPU clusters and high-performance networking.\n\n - Contributions to open-source ML systems or infrastructure projects.\n\n - Interest in publishing technical work or sharing insights through engineering blogs and technical writing.\n\n\n\n\nWHY THIS ROLE MATTERS\n\nThe next frontier in AI will not be unlocked by models alone. It will be unlocked by systems that let those models train faster, adapt continuously, and operate across real environments at scale.\n\nThat infrastructure does not exist yet in the form the world needs.\n\nWe’re building it.\n\n\n\n\nBENEFITS \u0026 PERKS\n\n - Cash Compensation Range of $150-350k, plus equity.\n\n - Flexible work arrangements, with the option to work remotely or in person from our San Francisco office.\n\n - Visa sponsorship and relocation support for international candidates.\n\n - Quarterly team offsites, hackathons, conferences, and learning opportunities.\n\n - A deeply technical, high-agency team working on infrastructure for open superintelligence.\n\nIf you’re excited about building the systems foundation for frontier-scale RL an","salary_min":150000,"salary_max":350000,"location":"San Francisco, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"senior","tags":["pytorch","search","distributed-systems","llm","gpu","agents","research","infrastructure"],"apply_url":"https://jobs.ashbyhq.com/PrimeIntellect/05e4b76b-2570-4c89-baf2-9833fff7378f/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-08T18:43:53.584Z","expires_at":"2026-08-15T14:10:47.919401Z","created_at":"2026-04-13T15:01:32.609376Z","updated_at":"2026-07-16T14:10:48.037879Z","company_name":"Prime Intellect","company_slug":"PrimeIntellect","company_logo_url":"https://www.google.com/s2/favicons?domain=primeintellect.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/537b089a-1139-46c6-9166-2dc6b9693a2f"},{"id":"2d9fb70b-e1df-4ef9-b1ba-f021f1b7f44a","company_id":"3da82454-107f-427f-88e7-01f315ef93fb","title":"Research Engineer - Reinforcement Learning","slug":"research-engineer-reinforcement-learning-6acec267","description":"OWN YOUR INTELLIGENCE\n\n\n\nPrime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team.\n\n\n\nOur platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale - from SFT and RL to tool use, agent workflows, and continuously improving production models. We are building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research, and the full-stack platform our own research team uses to build them. The next generation of AI companies, enterprises, and research teams do not just need more GPUs. They need the ability to turn their own workflows, tools, data, and feedback loops into superintelligence they own.\n\n\n\nPrime Intellect has raised $150M in total funding from Founders Fund, Radical Ventures, NVIDIA, and exceptional AI, infrastructure, and enterprise operators — including Andrej Karpathy, Dwarkesh Patel, and leaders and founders from Ramp, Perplexity, Harvey, Mercor, Zapier, Datadog, Cognition, OpenAI, Thinking Machines, Together AI, SemiAnalysis, LangChain, Browserbase, Cloudflare, Sierra, Databricks, Airbnb, OpenRouter, Standard Intelligence, Fleet, Core Auto, and more. We are looking for people who want to build at the intersection of frontier research, real infrastructure, and go-to-market for a category that does not fully exist yet.\n\n\n\n\nRESPONSIBILITIES\n\n - Lead and participate in novel research to build a massive scale synthetic data generation pipeline and orchestration solution\n\n - Optimize the performance, cost, and resource utilization of AI inference workloads by leveraging the most recent advances for compute \u0026 memory optimization techniques.\n\n - Contribute to the development of our open-source libraries and frameworks for synthetic data generation and distributed RL frameworks.\n\n - Publish research in top-tier AI conferences such as ICML \u0026 NeurIPS.\n\n - Distill highly technical project outcomes in layman approachable technical blogs to our customers and developers.\n\n - Stay up-to-date with the latest advancements in AI/ML infrastructure and tools, synthetic data gen research and proactively identify opportunities to enhance our platform's capabilities and user experience.\n\n\nREQUIREMENTS\n\n - Strong background in AI/ML engineering, with extensive experience in designing and implementing end-to-end pipelines for the inference or training of large-scale AI models.\n\n - Deep expertise in distributed inference techniques and frameworks (e.g. vllm, sglang) for optimizing the performance and scalability of AI workloads.\n\n - Solid understanding of MLOps best practices, including model versioning, experiment tracking, and continuous integration/deployment (CI/CD) pipelines.\n\n - Passion for advancing the state-of-the-art in reasoning and democratizing access to AI capabilities for researchers, developers, and businesses worldwide.\n\n - If you're not familiar with these, but feel like that you can contribute to our mission and you're a high-energy person, get familiar with these resources (here https://a.co/d/frW8MHY, here https://a.co/d/4WRhR0Y and here https://github.com/stas00/ml-engineering/tree/master) and please reach out!\n\n\nBENEFITS \u0026 PERKS\n\n - Cash Compensation Range of $150-350k, including equity incentives, aligning your success with the growth and impact of Prime Intellect.\n\n - Flexible work arrangements, with the option to work remotely or in-person at our offices in San Francisco.\n\n - Visa sponsorship and relocation assistance for international candidates.\n\n - Quarterly team off-sites, hackathons, conferences and learning opportunities.\n\n - Opportunity to work with a talented, hard-working and mission-driven team, united by a shared passion for leveraging technology to accelerate science and AI.\n\n\n\nIf you're excited about the opportunity to build the foundation for the future of decentralized AI and create a platform that empowers developers and researchers to push the boundaries of what's possible, we'd love to hear from you.","salary_min":150000,"salary_max":350000,"location":"San Francisco, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"senior","tags":["search","llm","agents","mlops","reinforcement-learning","research"],"apply_url":"https://jobs.ashbyhq.com/PrimeIntellect/ee13090e-3fea-40f0-b785-19316f52bf08/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-08T18:43:44.607Z","expires_at":"2026-08-15T14:10:46.569142Z","created_at":"2026-04-13T15:01:32.560515Z","updated_at":"2026-07-16T14:10:46.797256Z","company_name":"Prime Intellect","company_slug":"PrimeIntellect","company_logo_url":"https://www.google.com/s2/favicons?domain=primeintellect.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/2d9fb70b-e1df-4ef9-b1ba-f021f1b7f44a"},{"id":"8c402485-1400-4e3b-aacf-eaa1ab3b5dfb","company_id":"3da82454-107f-427f-88e7-01f315ef93fb","title":"Research Engineer - Distributed Training","slug":"research-engineer-distributed-training-19cda6e4","description":"OWN YOUR INTELLIGENCE\n\n\n\nPrime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team.\n\n\n\nOur platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale - from SFT and RL to tool use, agent workflows, and continuously improving production models. We are building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research, and the full-stack platform our own research team uses to build them. The next generation of AI companies, enterprises, and research teams do not just need more GPUs. They need the ability to turn their own workflows, tools, data, and feedback loops into superintelligence they own.\n\nWe train open frontier models and ship the same stack to our customers. Its spans the full stack of training, deploying and continuously improving models — compute, large-scale RL, environments, sandboxes, evals, and deployment.\n\n\n\nPrime Intellect has raised $150M in total funding from Founders Fund, Radical Ventures, NVIDIA, and exceptional AI, infrastructure, and enterprise operators — including Andrej Karpathy, Dwarkesh Patel, and leaders and founders from Ramp, Perplexity, Harvey, Mercor, Zapier, Datadog, Semianalysis, Cognition, OpenAI, Thinking Machines, Together AI, SemiAnalysis, LangChain, Browserbase, Cloudflare, Sierra, Databricks, Airbnb, OpenRouter, Standard Intelligence, Fleet, Core Auto, and more. We are looking for people who want to build at the intersection of frontier research, real infrastructure, and go-to-market for a category that does not fully exist yet.\n\n\n\n\nWHAT YOU’LL WORK ON\n\n - Build and optimize the distributed training infrastructure behind our pre-training and large-scale RL training workloads by contributing to our prime-rl https://github.com/PrimeIntellect-ai/prime-rl framework.\n\n - Improve end-to-end training efficiency across compute, memory, networking, and scheduling layers.\n\n - Design and implement low-level performance optimizations, including kernels, communication paths, and runtime improvements.\n\n - Work on distributed training systems spanning data, tensor, and pipeline parallel workloads.\n\n - Help shape the architecture of our RL training stack, including async rollout and post-training systems.\n\n - Contribute to open-source libraries and internal infrastructure used for frontier-scale model training.\n\n - Collaborate closely with researchers and infrastructure engineers to translate bottlenecks into concrete systems improvements.\n\n - Stay at the frontier of training systems, inference systems, compiler/runtime tooling, and hardware-aware optimization techniques.\n\n\n\n\n\nYOU MAY BE A FIT IF YOU HAVE\n\n - Strong systems engineering experience in AI/ML infrastructure, especially around large-scale model training or inference.\n\n - Deep familiarity with PyTorch and distributed training frameworks such as PyTorch Distributed, DeepSpeed, FSDP, Megatron, vLLM, Ray, or related tooling.\n\n - Experience optimizing training performance across kernels, memory movement, communication overhead, or parallelization strategy.\n\n - Hands-on experience with large-scale training techniques including data parallelism, tensor parallelism, and pipeline parallelism.\n\n - Strong understanding of GPU architecture, profiling, and performance debugging.\n\n - Ability to identify bottlenecks across the stack and drive improvements from first principles.\n\n - Comfort working in a fast-moving environment with ambiguous problems and high ownership.\n\n\n\n\nESPECIALLY EXCITING\n\n - Experience writing or optimizing CUDA / Triton kernels.\n\n - Experience with compiler or runtime optimization for ML systems.\n\n - Experience working on RL training infrastructure, rollout systems, or asynchronous training pipelines.\n\n - Experience with multi-node GPU clusters and high-performance networking.\n\n - Contributions to open-source ML systems or infrastructure projects.\n\n - Interest in publishing technical work or sharing insights through engineering blogs and technical writing.\n\n\n\n\n\n\n\nBENEFITS \u0026 PERKS\n\n - Cash Compensation Range of $150-350k, plus equity incentives, aligning your success with the growth and impact of Prime Intellect.\n\n - Flexible work arrangements, with the option to work remotely or in-person at our offices in San Francisco.\n\n - Visa sponsorship and relocation assistance for international candidates.\n\n - Quarterly team off-sites, hackathons, conferences and learning opportunities.\n\n - Opportunity to work with a talented, hard-working and mission-driven team, united by a shared passion for leveraging technology to accelerate science and AI.\n\nIf you’re excited about building the systems foundation for frontier-scale training and open superintelligence, we’d love to hear from you.","salary_min":150000,"salary_max":350000,"location":"San Francisco, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"senior","tags":["pre-training","search","agents","llm","pytorch","gpu","distributed-systems","research"],"apply_url":"https://jobs.ashbyhq.com/PrimeIntellect/8bd52610-175c-42a7-a7cd-b29c45f9d305/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-08T18:43:34.749Z","expires_at":"2026-08-15T14:10:46.400006Z","created_at":"2026-04-13T15:01:32.550978Z","updated_at":"2026-07-16T14:10:46.52985Z","company_name":"Prime Intellect","company_slug":"PrimeIntellect","company_logo_url":"https://www.google.com/s2/favicons?domain=primeintellect.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/8c402485-1400-4e3b-aacf-eaa1ab3b5dfb"},{"id":"e4025ddd-fba3-4d70-863f-4b95346b9c5c","company_id":"3da82454-107f-427f-88e7-01f315ef93fb","title":"Applied Research - Forward-Deployed","slug":"applied-research-forward-deployed-a5dcad80","description":"OWN YOUR INTELLIGENCE\n\n\n\nPrime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team.\n\n\n\nOur platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale - from SFT and RL to tool use, agent workflows, and continuously improving production models. We are building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research, and the full-stack platform our own research team uses to build them. The next generation of AI companies, enterprises, and research teams do not just need more GPUs. They need the ability to turn their own workflows, tools, data, and feedback loops into superintelligence they own.\n\n\n\nPrime Intellect has raised $150M in total funding from Founders Fund, Radical Ventures, NVIDIA, and exceptional AI, infrastructure, and enterprise operators — including Andrej Karpathy, Dwarkesh Patel, and leaders and founders from Ramp, Perplexity, Harvey, Mercor, Zapier, Datadog, Cognition, OpenAI, Thinking Machines, Together AI, SemiAnalysis, LangChain, Browserbase, Cloudflare, Sierra, Databricks, Airbnb, OpenRouter, Standard Intelligence, Fleet, Core Auto, and more. We are looking for people who want to build at the intersection of frontier research, real infrastructure, and go-to-market for a category that does not fully exist yet.\n\n\n\nABOUT THE ROLE\n\nWe're looking for a Forward-Deployed Research Engineer (FDRE) to serve as the primary technical interface between Prime Intellect and our most important customers: AI companies, research labs, and enterprises running post-training and agentic RL on our platform.\n\nThis is not a traditional research role. You'll spend most of your time embedded with customers, understanding their models, workflows, and goals. Then, you'll translate those objectives into concrete training runs, environment designs, evaluation harnesses, and deployment recipes using the Lab stack. You are the person who makes the platform work in practice for real workloads.\n\nYou'll work closely with our research, product, and infrastructure teams to feed field insights back into the platform, shaping what we build next based on what customers actually need.\n\n\n\n\n\nWHAT YOU'LL DO\n\n\nCUSTOMER ENGAGEMENT \u0026 TECHNICAL DELIVERY\n\n - Embed directly with strategic customers to understand their agent architectures, failure modes, and product goals\n\n - Design and build custom RL environments, evaluation harnesses, and verifiers that capture what \"good\" looks like for each customer's domain\n\n - Architect agent scaffolding — tool use, multi-step reasoning, memory, sandbox execution — tailored to customer workflows\n\n - Configure and launch training runs on Lab, iterating on reward functions, rollout strategies, and evaluation criteria\n\n - Serve as the technical lead for engagements end-to-end: from discovery through deployed, improved models\n\n\nPLATFORM FEEDBACK \u0026 ECOSYSTEM\n\n - Identify repeatable patterns from customer engagements and codify them into reference implementations, templates, and documentation\n\n - Serve as the voice of the customer internally, shaping the roadmap for Lab, verifiers, the Environments Hub, and training infrastructure\n\n - Build high-quality examples and \"recipes\" that make it easy for new customers and open-source contributors to extend the stack\n\n - Contribute to technical content (blog posts, tutorials, case studies) that demonstrates real-world platform usage\n\n\nAPPLIED RESEARCH \u0026 EXPERIMENTATION\n\n - Develop novel evaluation methodologies for agentic behavior — multi-step reasoning, tool use correctness, recovery from failure, long-horizon task completion\n\n - Prototype and iterate on agent harnesses for real-world tasks: code generation, workflow automation, document processing, and more\n\n - Experiment with reward design, rubric construction, and environment shaping to improve training signal quality\n\n - Stay current on the frontier of agentic AI, evals, and post-training methods, and bring that knowledge directly into customer work\n\n\nWHAT WE'RE LOOKING FOR\n\n - Deep hands-on experience building, evaluating, or deploying LLM-based agents in the past 1–2 years — you've seen what breaks in production and know what good evals look like\n\n - Strong intuition for evaluation design: you can look at a customer's agent and quickly identify what to measure, how to construct a rubric, and where the reward signal is weak\n\n - Working understanding of RL and post-training concepts (GRPO, RLHF, reward modeling, SFT) — you don't need to have written a trainer from scratch, but you should understand what the knobs do and why they matter\n\n - Strong Python skills and comfort with the modern AI stack (Hugging Face, inference engines, agent frameworks)\n\n - Experience in a customer-facing or consulting-adjacent technical role, or as","salary_min":150000,"salary_max":300000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["code-generation","agents","reinforcement-learning","search","llm","research"],"apply_url":"https://jobs.ashbyhq.com/PrimeIntellect/73f42d73-f967-4082-b599-b8914135a6b3/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-08T18:34:29.004Z","expires_at":"2026-08-15T14:10:47.843546Z","created_at":"2026-04-13T15:01:32.600939Z","updated_at":"2026-07-16T14:10:47.961822Z","company_name":"Prime Intellect","company_slug":"PrimeIntellect","company_logo_url":"https://www.google.com/s2/favicons?domain=primeintellect.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e4025ddd-fba3-4d70-863f-4b95346b9c5c"},{"id":"d6456870-ff5c-4c3f-89d2-a6e8784670b8","company_id":"57a9b50d-a69a-4f6f-9acb-910495c3c359","title":"MTS, Research Engineer","slug":"mts-research-engineer-69babe33","description":"About Us: \n At Fireworks, we’re building the future of generative AI infrastructure. Our platform delivers the highest-quality models with the fastest and most scalable inference in the industry. We’ve been independently benchmarked as the leader in LLM inference speed and are driving cutting-edge innovation through projects like our own function calling and multimodal models. Fireworks is a Series C company valued at $4 billion and backed by top investors including Benchmark, Sequoia, Lightspeed, Index, and Evantic. We’re an ambitious, collaborative team of builders, founded by veterans of Meta PyTorch and Google Vertex AI.\n About the Role \n We are looking for a Research Engineer to join our team, operating at the critical intersection of model research and training infrastructure.\n In this role, your time will be split between tackling open-ended research problems—such as designing novel architectures and improving algorithmic efficiency — and building the distributed training systems required to make those research breakthroughs a reality. You won't just be handed a paper to implement; you will be expected to reproduce state-of-the-art results from the literature, identify their limitations, and build the infrastructure needed to push beyond them.\n The most significant advances in deep learning require massive scale. We need engineers who are as comfortable reasoning about gradient descent and loss landscapes as they are about distributed systems, GPU cluster utilization, and data pipelines.\n  \n What You'll Do \n \n Conduct Open-Ended Research: Explore new model architectures, training objectives, and optimization techniques. Formulate hypotheses, design experiments, and iterate quickly based on empirical results.\n Reproduce and Extend State-of-the-Art: Implement and reproduce results from recent machine learning papers. Identify bottlenecks, propose improvements, and scale these methods to larger datasets and models.\n Build and Scale Training Infrastructure: Design, implement, and maintain high-performance, distributed machine learning systems. Optimize training loops, data loaders, and communication overhead across large GPU clusters.\n Bridge Science and Engineering: Translate abstract mathematical concepts and research ideas into robust, bug-free, and efficient code.\n Collaborate Cross-Functionally: Work closely with Research Scientists to unblock their experiments by providing tooling, optimizing code, and co-designing experiments that are hardware-aware.\n \n We Expect You To Have: \n \n Strong programming skills (Python, C++, or Rust) and a commitment to writing clean, maintainable code.\n Deep practical knowledge of machine learning frameworks (PyTorch, JAX, or TensorFlow).\n Experience working with large distributed systems and parallel computing (e.g., CUDA, NCCL, MPI).\n A strong foundation in linear algebra, calculus, probability, and statistics.\n A proven track record of implementing complex deep learning algorithms from scratch.\n \n Nice to Have: \n \n A Master’s or PhD in Computer Science, Machine Learning, Physics, Mathematics, or a related field (or equivalent industry experience).\n Experience with low-level GPU programming (CUDA/Triton) or hardware co-design.\n Familiarity with the challenges of training Large Language Models (LLMs)\n Familiarity with the challenges of inference, and OSS inference engines such as SGLang and vLLM\n Total compensation for this role also includes meaningful equity in a fast-growing startup, along with a competitive salary and comprehensive benefits package. Base salary is determined by a range of factors including individual qualifications, experience, skills, interview performance, market data, and work location. The listed salary range is intended as a guideline and may be adjusted.\n Base Pay Range (Plus Equity)\n $250,000 — $400,000 USD \n Why Fireworks AI? \n \n Solve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving.\n Build What’s Next: Work with bleeding-edge technology that impacts how businesses and developers harness AI globally.\n Ownership \u0026 Impact: Join a fast-growing, passionate team where your work directly shapes the future of AI—no bureaucracy, just results.\n Learn from the Best: Collaborate with world-class engineers and AI researchers who thrive on curiosity and innovation.\n \n Fireworks AI is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all innovators.","salary_min":250000,"salary_max":400000,"location":"New York, NY","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["pytorch","distributed-systems","tensorflow","data-pipeline","mlops","gpu","search","generative-ai"],"apply_url":"https://job-boards.greenhouse.io/fireworksai/jobs/4308305009","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-08T01:55:35Z","expires_at":"2026-08-15T14:02:25.096312Z","created_at":"2026-07-09T14:02:13.613892Z","updated_at":"2026-07-16T14:02:25.217607Z","company_name":"Fireworks AI","company_slug":"fireworks-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=fireworks.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/d6456870-ff5c-4c3f-89d2-a6e8784670b8"},{"id":"7623b85a-8839-4955-9ce5-579b61d832b9","company_id":"d3f1a010-47af-48d2-8b4e-a5953078daac","title":"Staff Product Manager, Vault","slug":"staff-product-manager-vault-22b85be0","description":"WHY HARVEY\n\nAt Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come.\n\nThis is a rare chance to help build a generational company at a true inflection point. With 1500+ customers in 60+ countries, strong product-market fit, and world-class investor support, we’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched.\n\nOur team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you.\n\nAt Harvey, the future of professional services is being written today — and we’re just getting started.\n\n\n\n\nROLE OVERVIEW\n\nAs a Staff Product Manager for Vault at Harvey, you will own and drive the product vision, strategy, and execution for our document and knowledge management platform-a critical foundation that powers how legal professionals interact with Harvey's AI capabilities. Vault enables law firms and enterprise clients to securely store, organize, search, and leverage their documents and institutional knowledge within Harvey's AI-powered ecosystem.\n\nYou will lead cross-functional teams to build and scale Vault's capabilities, including file management, AI-enabled search and Q\u0026A, document extraction, secure sharing across workspaces, and seamless integrations with enterprise document management systems like iManage, SharePoint, and Dropbox. This is a high-impact, high-visibility role where you'll directly shape how the world's leading law firms and enterprises organize and unlock value from their most sensitive documents.\n\n\n\n\n\n\n\nWHAT YOU'LL DO\n\n - Define and own the Vault product vision and roadmap: Develop a compelling product strategy that aligns with Harvey's mission to transform legal and professional services through AI, balancing near-term customer needs with long-term platform investments.\n\n - Drive end-to-end product development: Lead cross-functional teams including engineering, design, AI/ML, and go-to-market to ship high-quality features from ideation through launch and iteration.\n\n - Partner deeply with engineering: Work closely with engineering leadership to make technical and architectural decisions, dive deep into complex problems, and ensure we're building scalable, secure, and performant systems.\n\n - Understand and champion customer needs: Collaborate with Customer Success, Sales, and Legal Product Specialists to deeply understand how law firms and enterprise clients use Vault, identify adoption barriers, and translate insights into product improvements.\n\n - Own enterprise integrations strategy: Drive the vision for how Vault integrates with document management systems (iManage, SharePoint, Dropbox, Box) and other enterprise tools, ensuring seamless workflows for customers.\n\n - Define and track success metrics: Establish KPIs for Vault adoption, engagement, and value realization; use data to inform product decisions and demonstrate customer impact.\n\n - Collaborate cross-functionally: Partner with Product Marketing, Partnerships, and Admin/Governance teams to ensure successful product launches, effective positioning, and alignment with broader platform capabilities.\n\n - Contribute to Harvey's product culture: Mentor other product managers, share learnings, and help establish best practices as the product organization scales.\n\n\n\n\n\n\n\nWHAT YOU HAVE\n\nMust-Have Qualifications:\n\n - 8+ years of product management experience, with at least 3 years at a Staff or Senior PM level at a high-growth technology company.\n\n - Proven track record of owning and scaling complex platform or infrastructure products, ideally in enterprise SaaS, document management, knowledge management, or data-intensive domains.\n\n - Strong technical acumen with the ability to engage deeply with engineering on system design, distributed systems, data pipelines, and retrieval architectures.\n\n - Experience building products that handle sensitive data with robust security, compliance, and access control requirements.\n\n - Excellent communication skills with the ability to influence stakeholders at various levels, from engineers to executives.\n\n - Demonstrated ability to thrive in ambiguous, fast-paced environments and drive clarity through complexity.\n\n - Strong product sense and attention to detail-ability to think through both high-level strategy and nitty-gritty implementation details.\n\nPr","salary_min":177700,"salary_max":266500,"location":"New York, NY","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"lead","tags":["agents","legal","distributed-systems","data-pipeline","search"],"apply_url":"https://jobs.ashbyhq.com/harvey/d1669780-6956-4e5b-bc84-590267c565ea/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-07T23:22:44.243Z","expires_at":"2026-08-15T14:02:46.950505Z","created_at":"2026-07-09T14:02:30.476517Z","updated_at":"2026-07-16T14:02:47.085162Z","company_name":"Harvey AI","company_slug":"harvey-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=harvey.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/7623b85a-8839-4955-9ce5-579b61d832b9"},{"id":"03758f41-63b3-4634-840e-e1f59c2b8c5d","company_id":"d3f1a010-47af-48d2-8b4e-a5953078daac","title":"Staff Product Manager, Vault","slug":"staff-product-manager-vault-824f7ec9","description":"WHY HARVEY\n\nAt Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come.\n\nThis is a rare chance to help build a generational company at a true inflection point. With 1500+ customers in 60+ countries, strong product-market fit, and world-class investor support, we’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched.\n\nOur team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you.\n\nAt Harvey, the future of professional services is being written today — and we’re just getting started.\n\n\n\n\nROLE OVERVIEW\n\nAs a Staff Product Manager for Vault at Harvey, you will own and drive the product vision, strategy, and execution for our document and knowledge management platform-a critical foundation that powers how legal professionals interact with Harvey's AI capabilities. Vault enables law firms and enterprise clients to securely store, organize, search, and leverage their documents and institutional knowledge within Harvey's AI-powered ecosystem.\n\nYou will lead cross-functional teams to build and scale Vault's capabilities, including file management, AI-enabled search and Q\u0026A, document extraction, secure sharing across workspaces, and seamless integrations with enterprise document management systems like iManage, SharePoint, and Dropbox. This is a high-impact, high-visibility role where you'll directly shape how the world's leading law firms and enterprises organize and unlock value from their most sensitive documents.\n\n\n\n\n\n\n\nWHAT YOU'LL DO\n\n - Define and own the Vault product vision and roadmap: Develop a compelling product strategy that aligns with Harvey's mission to transform legal and professional services through AI, balancing near-term customer needs with long-term platform investments.\n\n - Drive end-to-end product development: Lead cross-functional teams including engineering, design, AI/ML, and go-to-market to ship high-quality features from ideation through launch and iteration.\n\n - Partner deeply with engineering: Work closely with engineering leadership to make technical and architectural decisions, dive deep into complex problems, and ensure we're building scalable, secure, and performant systems.\n\n - Understand and champion customer needs: Collaborate with Customer Success, Sales, and Legal Product Specialists to deeply understand how law firms and enterprise clients use Vault, identify adoption barriers, and translate insights into product improvements.\n\n - Own enterprise integrations strategy: Drive the vision for how Vault integrates with document management systems (iManage, SharePoint, Dropbox, Box) and other enterprise tools, ensuring seamless workflows for customers.\n\n - Define and track success metrics: Establish KPIs for Vault adoption, engagement, and value realization; use data to inform product decisions and demonstrate customer impact.\n\n - Collaborate cross-functionally: Partner with Product Marketing, Partnerships, and Admin/Governance teams to ensure successful product launches, effective positioning, and alignment with broader platform capabilities.\n\n - Contribute to Harvey's product culture: Mentor other product managers, share learnings, and help establish best practices as the product organization scales.\n\n\n\n\n\n\n\nWHAT YOU HAVE\n\nMust-Have Qualifications:\n\n - 8+ years of product management experience, with at least 3 years at a Staff or Senior PM level at a high-growth technology company.\n\n - Proven track record of owning and scaling complex platform or infrastructure products, ideally in enterprise SaaS, document management, knowledge management, or data-intensive domains.\n\n - Strong technical acumen with the ability to engage deeply with engineering on system design, distributed systems, data pipelines, and retrieval architectures.\n\n - Experience building products that handle sensitive data with robust security, compliance, and access control requirements.\n\n - Excellent communication skills with the ability to influence stakeholders at various levels, from engineers to executives.\n\n - Demonstrated ability to thrive in ambiguous, fast-paced environments and drive clarity through complexity.\n\n - Strong product sense and attention to detail-ability to think through both high-level strategy and nitty-gritty implementation details.\n\nPr","salary_min":177700,"salary_max":266500,"location":"San Francisco, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"lead","tags":["agents","data-pipeline","search","distributed-systems","legal"],"apply_url":"https://jobs.ashbyhq.com/harvey/85de4e2f-9ece-40a6-bea4-3ee94bba6c97/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-07T23:22:41.021Z","expires_at":"2026-08-15T14:02:46.869988Z","created_at":"2026-07-09T14:02:30.396235Z","updated_at":"2026-07-16T14:02:46.992096Z","company_name":"Harvey AI","company_slug":"harvey-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=harvey.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/03758f41-63b3-4634-840e-e1f59c2b8c5d"},{"id":"9bf087cc-bea6-427a-bbb1-aa8de95d7ad9","company_id":"aa372131-86ce-432a-af45-e2b42a79ba29","title":"Research Engineers, Post-Training","slug":"research-engineers-post-training-1fd6b6d1","description":"ABOUT DISTYL AI\n\nDistyl is an applied AI technology company partnering with the world’s most ambitious institutions to rearchitect critical operations for the frontier of AI. Our customers include the largest companies in telecom, healthcare, insurance, manufacturing, consumer goods, and global social organizations.\n\nWe research and deploy technologies that power AI-native operations — both for our partners and for Distyl itself. Our work spans research into self-constructing systems, the development of the most reliable execution of AI systems, and products that transform mission-critical workflows. As a result, Distyl's technologies affect some of the world's largest operations — from hundreds of millions of consumer interactions to tens of millions of supply chain transactions and millions of patient journeys.\n\nDistyl is backed by leading investors including Lightspeed Venture Partners, Khosla Ventures, Coatue, DST Global, and the board-members of 20+ F500s. The results reflect this approach: a 100% production deployment success rate for our customers and one of the few enterprise AI companies to run a profitable business.\n\n\n\n\nWHAT WE ARE LOOKING FOR\n\nAt Distyl, Research Engineers build the bridge between frontier AI research and production systems that deliver real business value. This role is for engineers who are excited to investigate how AI systems should be designed, rapidly prototype new ideas, and turn promising concepts into reliable systems that work inside real customer environments.\n\n\n\nResearch Engineers operate at the intersection of applied research, systems engineering, and customer-facing deployment. They design and implement compound AI systems, run experiments to understand system behavior, build evaluation frameworks, and collaborate closely with AI Researchers, AI Engineers, and customer stakeholders. Their work is not limited to demos or isolated prototypes: they help turn new techniques into robust systems that can be measured, operated, and improved in production.\n\n\n\n\nKEY RESPONSIBILITIES\n\n - Design and run post-training workflows that improve the behavior, reliability, and usefulness of AI systems\n\n - Develop datasets, preference signals, evaluation suites, reward models, fine-tuning workflows, and feedback loops for applied AI use cases\n\n - Investigate how different post-training techniques affect system behavior across enterprise workflows and production constraints\n\n - Build infrastructure for experimentation, model comparison, regression testing, and behavior analysis\n\n - Partner with AI Researchers to explore new post-training methods and with AI Engineers to apply successful techniques in deployed systems\n\n - Analyze model outputs, failure modes, human feedback, and production traces to identify opportunities for behavioral improvement\n\n - Create repeatable processes for adapting AI systems to customer domains while preserving robustness, transparency, and maintainability\n\n - Communicate clearly with internal teams and customer stakeholders about model behavior, evaluation results, limitations, and tradeoffs\n\n\n\n\nWHO YOU ARE\n\n - Experience Improving Model Behavior: You have worked with fine-tuning, preference optimization, reinforcement learning, reward modeling, synthetic data, evals, or related post-training techniques\n\n - Strong Programming and Experimentation Skills: You can build training and evaluation pipelines, run controlled experiments, analyze results, and iterate quickly\n\n - Research-Oriented Builder: You care about understanding why behavior changes, not just whether a benchmark improves\n\n - AI Systems Mindset: You understand that model behavior is shaped by data, prompts, tools, retrieval, evaluators, and deployment context—not model weights alone\n\n - AI-Native Working Style: You use AI tools daily to accelerate coding, analysis, debugging, experimentation, and research exploration\n\n - Bias Towards Measurement: You make behavioral improvements concrete through evaluations, comparisons, regression tests, and production-relevant metrics\n\n - Comfort with Applied Constraints: You can balance research ambition with practical constraints around cost, latency, reliability, data availability, and customer requirements\n\n - Ownership Mentality: You take responsibility for whether post-training work improves real system outcomes, not just offline scores\n\n\n\n\nWHAT WE OFFER\n\n - The base salary range for this role is $150K – $250K, depending on experience, location, and level. In addition to base compensation, this role is eligible for meaningful equity, along with a comprehensive benefits package\n\n - 100% coverage of medical, dental, and vision insurance for employee and dependents\n\n - Flexible time off\n\n - Retirement and financial planning benefits, including access to pre-tax HSA, FSA, and commuter accounts, 401(k), and financial coaching resources\n\n - Comprehensive wellness benefits, including physical fitness, mental well-being, and fertility and family-building","salary_min":150000,"salary_max":250000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["healthcare","reinforcement-learning","search","fine-tuning","research"],"apply_url":"https://jobs.ashbyhq.com/distyl/96951117-efef-4f27-bbc4-671559d4af30/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-07T15:39:17.759Z","expires_at":"2026-08-15T14:19:19.695276Z","created_at":"2026-06-28T14:17:24.151827Z","updated_at":"2026-07-16T14:19:19.829698Z","company_name":"Distyl AI","company_slug":"distyl-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=distyl.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/9bf087cc-bea6-427a-bbb1-aa8de95d7ad9"},{"id":"8f723451-d172-49ec-8f31-083cc8165ebe","company_id":"aa372131-86ce-432a-af45-e2b42a79ba29","title":"Research Engineers, Agents","slug":"research-engineers-agents-a9bfbf51","description":"ABOUT DISTYL AI\n\nDistyl is an applied AI technology company partnering with the world’s most ambitious institutions to rearchitect critical operations for the frontier of AI. Our customers include the largest companies in telecom, healthcare, insurance, manufacturing, consumer goods, and global social organizations.\n\nWe research and deploy technologies that power AI-native operations — both for our partners and for Distyl itself. Our work spans research into self-constructing systems, the development of the most reliable execution of AI systems, and products that transform mission-critical workflows. As a result, Distyl's technologies affect some of the world's largest operations — from hundreds of millions of consumer interactions to tens of millions of supply chain transactions and millions of patient journeys.\n\nDistyl is backed by leading investors including Lightspeed Venture Partners, Khosla Ventures, Coatue, DST Global, and the board-members of 20+ F500s. The results reflect this approach: a 100% production deployment success rate for our customers and one of the few enterprise AI companies to run a profitable business.\n\n\n\n\nWHAT WE ARE LOOKING FOR\n\nAt Distyl, Research Engineers build the bridge between frontier AI research and production systems that deliver real business value. This role is for engineers who are excited to investigate how AI systems should be designed, rapidly prototype new ideas, and turn promising concepts into reliable systems that work inside real customer environments.\n\n\n\nResearch Engineers operate at the intersection of applied research, systems engineering, and customer-facing deployment. They design and implement compound AI systems, run experiments to understand system behavior, build evaluation frameworks, and collaborate closely with AI Researchers, AI Engineers, and customer stakeholders. Their work is not limited to demos or isolated prototypes: they help turn new techniques into robust systems that can be measured, operated, and improved in production.\n\n\n\n\nKEY RESPONSIBILITIES\n\n - Design, prototype, and implement agentic AI systems that perform reliably across complex enterprise workflows\n\n - Build compound AI architectures that combine planning, tool use, retrieval, memory, evaluation, orchestration, and execution\n\n - Investigate how agents reason, coordinate, recover from errors, and interact with external systems under real-world constraints\n\n - Develop evaluation frameworks that measure agent behavior, task completion, reliability, robustness, and failure modes\n\n - Create tools and abstractions that make agent behavior easier to observe, debug, test, and improve\n\n - Partner with AI Researchers to explore new agent architectures and with AI Engineers to harden successful approaches for production use\n\n - Integrate agents into customer APIs, applications, data platforms, and operational workflows\n\n - Communicate clearly with internal teams and customer stakeholders about agent capabilities, limitations, tradeoffs, and risks\n\n\n\n\nWHO YOU ARE\n\n - Experience Building Agentic Systems: You have built AI systems that use models, tools, retrieval, planning, memory, or multi-step execution to complete real tasks\n\n - Strong Engineering Fundamentals: You write clean, maintainable Python and are comfortable debugging complex, stateful systems\n\n - Systems-Level Reasoning: You think holistically about how prompts, tools, context, evaluators, state, orchestration, and external APIs interact\n\n - Research-Oriented Builder: You are curious about why agents succeed or fail, and you can design experiments to test different architectures and behaviors\n\n - AI-Native Working Style: You use AI tools daily to write code, debug systems, explore designs, analyze traces, and accelerate experimentation\n\n - Bias Towards Showing vs. Telling: You prefer working demonstrations, traces, evaluations, and production behavior over abstract descriptions\n\n - Comfort in Customer Environments: You can translate ambiguous business workflows into concrete agent designs and explain system behavior clearly to stakeholders\n\n - Ownership Mentality: You take responsibility for whether an agentic system performs reliably, safely, and usefully in production\n   \n   \n\n\nWHAT WE OFFER\n\n - The base salary range for this role is $150K – $250K, depending on experience, location, and level. 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