{"access":{"advertiser_pricing_url":"https://aidevboard.com/pricing","catalog_url":"https://aidevboard.com/api/v1/catalog","description":"Public read endpoints are open and free. API keys are optional for stable agent identity and keyed hourly throttling.","docs_url":"https://aidevboard.com/docs","mode":"open","register_url":"https://aidevboard.com/api/v1/register"},"degraded":false,"estimated":false,"has_next":true,"jobs":[{"id":"f8c6c621-b459-40f6-b41d-0baa191734ff","company_id":"a0000000-0000-0000-0000-000000000001","title":"Research Lead, Training Insights","slug":"research-lead-training-insights-6091f430","description":"About Anthropic \n Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n About the role \n As a Research Lead on the Training Insights team, you'll develop the strategy for, and lead execution on, how we measure and characterize model capabilities across training and deployment. This is a hands-on leadership role: you'll drive original research into new evaluation methodologies while leading a small team of researchers and research engineers doing the same.\n Your work will span the full lifecycle of model development. You'll research and build new long-horizon evaluations that test the boundaries of what our models can achieve, develop novel approaches to measuring emerging capabilities, and deepen our understanding of how those capabilities develop — both during production RL training and after. You'll also take a cross-organizational view, working across Reinforcement Learning, Pretraining, Inference, Product, Alignment, Safeguards, and other teams to map the landscape of model evaluations at Anthropic and identify critical gaps in coverage.\n This role carries significant visibility and impact. You'll help shape the evaluation narrative for model releases, contributing directly to how Anthropic communicates about its models to both internal and external audiences. Done well, you will change how the industry measures and understands model capabilities, significantly furthering our safety mission.  \n Responsibilities:  \n \n Build new novel and long-horizon evaluations\n Develop novel measurement approaches for understanding how model capabilities emerge and evolve during RL training\n Lead strategic evaluation coverage across the company\n Shape the evaluation narrative for model releases\n Lead and mentor a small team of researchers and research engineers, setting research direction and fostering a culture of rigorous, creative research\n Design evaluation frameworks that balance scientific rigor with the practical demands of production training schedules\n Build and maintain relationships across Anthropic's research organization to ensure evaluation insights inform training and deployment decisions\n Contribute to the broader research community through publications, open-source contributions, or external engagement on evaluation best practices\n \n You may be a good fit if you:  \n \n Have significant experience designing and running evaluations for large language models or similar complex ML systems\n Have led technical projects or teams, either formally or through sustained ownership of critical research directions\n Are equally comfortable designing experiments and writing code—you can move between research and implementation fluidly\n Think strategically about what to measure and why, not just how to measure it\n Can synthesize information across multiple teams and workstreams to form a coherent picture of model capabilities\n Communicate complex technical findings clearly to both technical and non-technical audiences\n Are results-oriented and thrive in fast-paced environments where priorities shift based on research findings\n Care deeply about AI safety and want your work to directly influence how capable AI systems are developed and deployed\n \n Strong candidates may also have:  \n \n Experience building evaluations for long-horizon or agentic tasks\n Deep familiarity with Reinforcement Learning training dynamics and how model behavior changes during training\n Published research in machine learning evaluation, benchmarking, or related areas\n Experience with safety evaluation frameworks and red teaming methodologies\n Background in psychometrics, experimental psychology, or other measurement-focused disciplines\n A track record of communicating evaluation results to inform high-stakes decisions about model development or deployment\n Experience managing or mentoring researchers and engineers\n \n Representative projects:  \n \n Designing and implementing a suite of long-horizon evaluations that test model capabilities on tasks requiring sustained reasoning, planning, and tool use over extended interactions\n Building systems to track capability development across RL training checkpoints, surfacing insights about when and how specific capabilities emerge\n Conducting a cross-org audit of evaluation coverage, identifying blind spots, and prioritizing new evaluations to fill critical gaps across Pretraining, RL, Inference, and Product\n Developing the evaluation methodology and narrative for a major model release, working with research leads and communications to clearly characterize model capabilities and limitations\n Researching and prototyping novel evaluation approaches for capabilities that are difficult to measure with existing benchmarks\n Leading a team","salary_min":850000,"salary_max":850000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["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":"b2503a2d-d800-43bc-9f84-11af04a6a4b4","company_id":"77beb456-fc80-40a4-b773-f0b17d1ece4c","title":"Generative AI - Graphics Engineer","slug":"generative-ai-graphics-engineer-429a27ce","description":"WHO YOU ARE\n\nWe are looking for skilled graphics engineer who have a deep command of modern C++ and GPU programming, a strong mathematical foundation, and an expert understanding of computer graphics—whether rendering, geometry processing, simulation, or advanced real-time techniques. You collaborate naturally with artists, researchers, and engineers, explaining complex ideas with clarity and learning from diverse perspectives. You’re not afraid of new ideas or unfamiliar pipelines. Most importantly, you’re excited to build the next generation of 3D creation technology—graphics systems that will empower millions of creators worldwide.\n\n\nWHO WE ARE\n\nAt Meshy, we believe 3D creation should be boundless and accessible. Our mission statement is simple: unleash creativity. We built a full pipeline for 3D content ranging from text / image to 3D, texturing, texture editing, animation rigging, etc. We also built a vibrant community for our creators, where people can share their work, take inspiration from others, and even use it as an asset marketplace for their games and prototypes. We are the market leader in 3D generative AI, recognized as the No.1 in popularity among 3D AI tools (according to 2024 A16Z Games survey), and we generate real value and is used by enterprises (including Meta, Square Enix, Deepmind, etc.) and millions of end users. Meshy is used in game and film production, in 3D printing, in industrial product design, in enablement of novel product features such as user-generated content, and even in training and simulation for robotics and physical AI.\n\n\nYOUR NEXT CHALLENGE\n\nAs a core member of Meshy’s algorithm team, you will design and build the next generation of high-performance graphics systems that power our 3D generative AI training and products. You will collaborate closely with graphics experts, generative AI researchers, and infrastructure engineers to enable new creative capabilities and push the boundaries of what AI-empowered 3D pipelines can achieve.\n\n \n\nIn this role, you will:\n\n - Build and optimize high-performance graphics components—rendering kernels, geometry processing operators, and supporting systems.\n\n - Develop robust production-quality pipelines that integrate with data pipelines, generative models and artist-facing applications.\n\n - Work across GPU clusters, cloud environments, and local DCC tools to ensure seamless interoperability and scalability.\n\n - Collaborate closely with artists, product teams, and ML researchers to translate creative requirements into technical implementations.\n\n - Contribute to internal tooling, demos, documentation, open-source initiatives, or technical reports that elevate Meshy’s graphics capabilities.\n\n\nWHAT WE'RE LOOKING FOR\n\n - Expert-level C++ and GPU programming skills, with a strong ability to write high-performance, memory-efficient code.\n\n - Solid mathematical foundation with deep understanding of computer graphics—either rendering, geometry processing, or both.\n\n - Hands-on experience building production-grade graphics systems, such as rendering engines, geometry pipelines, asset tools, or similar large-scale systems.\n\n - Strong engineering discipline—clean code, reproducible results, rigorous profiling, and sustainable system design.\n\n - Working knowledge of major DCC tools (Houdini, Blender, Maya), including experience developing scripts, plug-ins, or custom tools within these environments is a plus.\n\n - Experience in AAA game development, VFX pipelines, or other high-end 3D production environments is a plus.\n\n - Demonstrated contributions to open-source graphics projects or publications in top-tier CG venues (SIGGRAPH, etc.) are pluses.\n\n\nA LITTLE MORE ABOUT MESHY.AI\n\nTrusted by Meta, Square Enix, Deepmind and more, Meshy is redefining 3D creation with generative AI. We empower artists, designers, engineers, hobbyists, and makers to bring immersive worlds, characters, and experiences to reality in minutes instead of months.\n\n \n\nIn addition to our core mission of unleashing creativity, we build a culture that we enjoy and are proud of. Here are some highlights:\n\n - We value intelligence and the pursuit of knowledge. We are a global team of generative-AI pioneers, computer-graphics veterans, and product builders who believe human expression and enjoyment is the ultimate frontier of computing.\n\n - We care deeply about our work, our users, and each other. Empathy and passion drive us forward. We have a culture of directness and truthfulness, therefore we value constructive criticism. Being direct and truthful is the most sincere form of trust and care.\n\n - We trust our instincts and are not afraid to take bold risks. Meshy was born from a few-hour prototype, a bold pivot for a team that had very little experience in AI. Innovation requires courage.\n\n - We have a keen eye for quality and aesthetics. Our products are not just functional but also beautiful. The same aesthetics permeate through our culture, our code and ","salary_min":175000,"salary_max":300000,"location":"San Francisco, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"senior","tags":["gpu","generative-ai","data-pipeline","robotics","computer-graphics","research"],"apply_url":"https://jobs.ashbyhq.com/meshy/e08ff336-379d-4cde-8df0-c5ab335517b3/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-15T20:04:34.461Z","expires_at":"2026-08-15T14:10:57.040052Z","created_at":"2026-07-16T14:10:57.180092Z","updated_at":"2026-07-16T14:10:57.180092Z","company_name":"Meshy","company_slug":"meshy","company_logo_url":"https://www.google.com/s2/favicons?domain=meshy.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/b2503a2d-d800-43bc-9f84-11af04a6a4b4"},{"id":"6db6f99f-a30e-4524-a8e4-b34154992b4d","company_id":"77beb456-fc80-40a4-b773-f0b17d1ece4c","title":"Generative AI - 3D Foundation Model","slug":"generative-ai-3d-foundation-model-cb2667cc","description":"WHO YOU ARE\n\nYou are a talented, hands-on researcher who thrives in a fast-paced environment, is self-directed, a team player, and knows how to get things done efficiently. You have deep understanding of the transformer architecture, have strong python and tensor programming skills, have a vision for AI beyond linear sequences, and you believe in \"the scaling law\". You can translate high-level goals into concrete research and implementation steps, set an approach, and follow through. When it's time to explain your ideas, you bring clarity to complex technical issues. You are not afraid of confronting new ideas, and you are eager to share your knowledge with the team. You use these skills to create real-world benefits for our researchers, engineers, and millions of users, and you are excited to help advance our effort to push the state of the art of AI that understands and generates 3D worlds.\n\n\nWHO WE ARE\n\nAt Meshy, we believe 3D creation should be boundless and accessible. Our mission statement is simple: unleash creativity. We built a full pipeline for 3D content ranging from text / image to 3D, texturing, texture editing, animation rigging, etc. We also built a vibrant community for our creators, where people can share their work, take inspiration from others, and even use it as an asset marketplace for their games and prototypes. We are the market leader in 3D generative AI, recognized as the No.1 in popularity among 3D AI tools (according to 2024 A16Z Games survey), and we generate real value and is used by enterprises (including Meta, Square Enix, Deepmind, etc.) and millions of end users. Meshy is used in game and film production, in 3D printing, in industrial product design, in enablement of novel product features such as user-generated content, and even in training and simulation for robotics and physical AI.\n\n\nYOUR NEXT CHALLENGE\n\nAs a core member of the team of research scientists and machine learning engineers at Meshy, you will drive the development of our core 3D-native generative foundational model. In this role, you will join our foundational research to advance 3D AI, apply learnings from other fields of ML, and pushing the state of the art. You will also work towards long-term ambitious research goals, while identifying intermediate milestones.\n\n \n\nThe essential functions include, but are not limited to the following:\n\n - Design, train, and refine large-scale 3D generative models from covering pre-training, post-training, and emerging paradigms in diffusion, flow matching, and multi-modal learning.\n\n - Bridge the gap between cutting-edge research and product, deploy models in real products used by millions of creators, using human feedback and creative evaluation.\n\n - Create novel model architectures to make 3D generation faster, higher-quality, and more controllable.\n\n - Collaborate with infrastructure and systems teams to build scalable training, and data pipelines across GPU clusters and cloud environments.\n\n - Bring engineering discipline into an fast-paced research environment: elegant code, reproducible experiments, and building software as a team.\n\n - Share insights and breakthroughs through internal demos, open-source contributions, or technical reports that advance the field of 3D generative AI.\n\n\nWHAT WE'RE LOOKING FOR\n\n - Strong engineering skills in Python and deep learning frameworks (preferably PyTorch); comfortable moving between research prototypes and production systems.\n\n - Familiar with Transformers and modern generative AI models (Diffusion / flow matching, VAE, etc.).\n\n - Curiosity and passion for multi-modal AI, and have an intuitive understanding of how models perceive, represent, and generate 3D worlds.\n\n - Familiar with high performance training on large scale infrastructure (e.g., SLURM, Ray, k8s) is a plus.\n\n - Contributions to popular open-source machine learning projects or publications in top-tier CV / ML conferences is a plus.\n\n\nA LITTLE MORE ABOUT MESHY.AI\n\nTrusted by Meta, Square Enix, Deepmind and more, Meshy is redefining 3D creation with generative AI. We empower artists, designers, engineers, hobbyists, and makers to bring immersive worlds, characters, and experiences to reality in minutes instead of months.\n\n \n\nIn addition to our core mission of unleashing creativity, we build a culture that we enjoy and are proud of. Here are some highlights:\n\n - We value intelligence and the pursuit of knowledge. We are a global team of generative-AI pioneers, computer-graphics veterans, and product builders who believe human expression and enjoyment is the ultimate frontier of computing.\n\n - We care deeply about our work, our users, and each other. Empathy and passion drive us forward. We have a culture of directness and truthfulness, therefore we value constructive criticism. Being direct and truthful is the most sincere form of trust and care.\n\n - We trust our instincts and are not afraid to take bold risks. Meshy was born from a few-hour prototype, a bold pivot","salary_min":175000,"salary_max":300000,"location":"San Francisco, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"senior","tags":["generative-ai","gpu","robotics","deep-learning","data-pipeline","pre-training","pytorch","research"],"apply_url":"https://jobs.ashbyhq.com/meshy/f52aa172-0212-4db8-a93d-406b910b9fea/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-15T19:18:36.925Z","expires_at":"2026-08-15T14:10:56.873764Z","created_at":"2026-07-16T14:10:56.993732Z","updated_at":"2026-07-16T14:10:56.993732Z","company_name":"Meshy","company_slug":"meshy","company_logo_url":"https://www.google.com/s2/favicons?domain=meshy.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/6db6f99f-a30e-4524-a8e4-b34154992b4d"},{"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":"5e3167da-1058-431f-8718-8bb9f1e4656f","company_id":"3d233526-89a8-48ea-b0ed-3304a35b8acf","title":"Software Engineer II, ML Ops","slug":"software-engineer-ii-ml-ops-461a0523","description":"At WHOOP, we're on a mission to unlock human performance. WHOOP empowers members to perform at a higher level through a deeper understanding of their bodies and daily lives.\nWe are looking for a talented and passionate Software Engineer II to join our MLOps team, focusing on the development and optimization of ML cloud infrastructure. In this role, you will play a critical part in supporting our Data Science and AI teams by building robust, scalable systems for the productionalization of machine learning models. Your work will be at the heart of bringing advanced ML/AI solutions into production, ensuring they are reliable, scalable, and ready to drive value across WHOOP.\n","salary_min":125000,"salary_max":175000,"location":"Boston, MA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"mid","tags":["cloud","mlops","machine-learning","research"],"apply_url":"https://jobs.lever.co/whoop/82635467-6cfb-4e8b-967e-73355a0d0b8f/apply","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-13T16:24:21.247Z","expires_at":"2026-08-15T14:17:41.659491Z","created_at":"2026-07-15T14:19:08.388557Z","updated_at":"2026-07-16T14:17:41.774926Z","company_name":"WHOOP","company_slug":"whoop","company_logo_url":"https://www.google.com/s2/favicons?domain=whoop.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/5e3167da-1058-431f-8718-8bb9f1e4656f"},{"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":"3b5bd89e-dae1-44b9-a41a-2e1a729363f6","company_id":"6734f15a-40ed-4186-ae4a-d774c655ae58","title":"Principal Scientist / Associate Director, Agentic AI Research for Materials Science","slug":"principal-scientist-associate-director-agentic-ai-research-for-materials-science-e225ff03","description":"Your Impact at LILA \n Own the technical direction for agentic AI systems applied to materials science at Lila. You will set and execute the roadmap for autonomous agents that plan, run, and interpret materials experiments, based on understanding of internal knowledge and state-of-the-art research work in public literature. Your work shifts materials research from human-paced iteration to machine-paced experimentation through scientific reasoning and understanding.\n This is a player-coach role on the PS AI team. You will lead a small group of scientists and engineers, set the bar for scientific rigor and engineering quality, and partner with diverse teams so that agentic systems land on real programs. You will own the trade-offs between research ambition and production reliability, and represent the agentic-AI direction to technical leadership.\n The work spans foundational research and applied delivery. You will publish where the science merits it, ship systems that materials teams depend on, and shape how Lila scales agentic capabilities across its materials portfolio.\n What You'll Be Building \n \n Roadmap and direction. Define and execute the agentic AI roadmap for materials science, including agentic frameworks and retrieval-augmented generation for understanding multi-modal research data from research literature and other data sources.\n Agent system architecture. Lead the design of agentic frameworks grounded in fundamental scientific understanding and the state of the art, and deliver end-to-end systems on real-world projects.\n Team leadership. Hire, mentor, and grow a small cross-functional team of scientists and engineers; set the bar for scientific rigor, code quality, and reproducibility.\n Cross-team partnership. Partner with diverse teams at Lila to push the state of the art and deliver systems that integrate with experimental infrastructure and land on real programs.\n Research currency and external voice. Track state-of-the-art in agentic AI, scientific ML, data extraction, and reasoning models; translate external advances into internal direction, and publish or present where the science merits it.\n \n What You'll Need to Succeed \n \n PhD in Computer Science, Machine Learning, Materials Science, Chemistry, Physics, or a related field, with 5+ years of post-PhD research and applied ML experience.\n Track record of building and shipping agentic systems, ML pipelines, or autonomous research workflows that delivered measurable scientific or product impact.\n Deep expertise across modern ML, NLP, and reasoning: LLMs, agentic frameworks, tool use, planning, data extraction, and multi-modal data.\n Working knowledge of materials science, computational chemistry, or condensed-matter physics sufficient to ground agent behavior in real scientific constraints.\n Proficiency in Python and the ML software stack, with strong engineering habits around reproducibility, testing, and production deployment.\n Experience leading scientists and engineers: setting technical direction, hiring, mentoring, and developing team members.\n Clear written and verbal communication; able to translate between research, engineering, and program stakeholders.\n \n Bonus Points For \n \n Publications, patents, or open-source contributions in agentic AI, scientific ML, or autonomous research systems.\n Experience integrating agents with real-world materials science tasks and familiarity with materials data representations and ontologies.\n Production experience with workflow orchestration and distributed compute on cloud or HPC.\n Community recognition: invited talks, conference organizing, or community leadership in agentic AI or scientific AI.\n Compensation \n We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.\n U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.\n International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market.\n Expected Base Salary Range\n $288,000 — $420,000 USD \n About LILA \n Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.\n LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonom","salary_min":288000,"salary_max":420000,"location":"Boston, MA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["llm","nlp","agents","rag","research"],"apply_url":"https://job-boards.greenhouse.io/lilasciences/jobs/4273850009","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-10T13:33:45Z","expires_at":"2026-08-15T14:19:13.669806Z","created_at":"2026-07-10T14:18:13.983856Z","updated_at":"2026-07-16T14:19:13.793528Z","company_name":"Lila Sciences","company_slug":"lila-sciences","company_logo_url":"https://www.google.com/s2/favicons?domain=lila.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/3b5bd89e-dae1-44b9-a41a-2e1a729363f6"},{"id":"92eff494-6559-427b-8a18-9f3ed481a25a","company_id":"2114efab-ea67-411b-bfb8-7899153105f3","title":"Member of Technical Staff, CI/CD Infrastructure","slug":"member-of-technical-staff-cicd-infrastructure-1daba4be","description":"Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference efficient and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware—a position that took years to build.\n\n\n\n\nABOUT THE ROLE\n\nvLLM is growing at a fast pace, and every bit of that growth lands on the CI system. More models, more hardware, more contributors, more ways for things to break. Your job is to advance the CI system so it scales with vLLM’s momentum and unlocks faster development for everyone.\n\nYou’ll get to:\n\n - Maintain and scale the compute infrastructure that powers CI, release, performance benchmark, accuracy evaluation for vLLM project, across a wide range of models and accelerators including H100/H200, (G)B200/300, AMD MI325/355X, TPU, Intel Gaudi, etc..\n\n - Get creative about cutting CI time-to-signal from hours to minutes\n\n - Make sure every corner of vLLM code base is well-tested\n\n - Keep vLLM releases rock-solid\n\n - Build out tooling that helps 3,000+ vLLM contributors move fast\n\n\n\n\nSKILLS AND QUALIFICATIONS\n\nMinimum qualifications:\n\n - Strong experience with Docker, Kubernetes, and containerized build or test environments.\n\n - Built CI/CD pipelines from scratch using GitHub Actions, Buildkite, or similar systems.\n\n - Familiar with CI design patterns and CI techniques: compute orchestration, handling flaky tests, dependency/environment management, caching, remote execution, test target determination, etc, test coverage, and so on.\n\n - Fluent in Python, Bash, Go, or similar for automation and tooling.\n\n - Solid fundamentals of Linux, security, networking, storage, package management,.\n\nBonus points for:\n\n - Setting up infrastructure for ML, inference, CUDA, ROCm, or accelerator-heavy workloads.\n\n - Running Buildkite at scale, including agents, queues, dynamic pipelines, test sharding, caching, and artifact management.\n\n - Operating Kubernetes clusters for CI, batch jobs, test execution, or internal developer infrastructure.\n\n - Managing CI/CD in large open-source project\n\n - Building dashboards, alerts, runbooks, or tooling for CI observability.\n\n\nLOGISTICS\n\n - Location: This role is based in San Francisco, California. Will consider remote in the US for exceptional candidates.\n\n - Compensation: Depending on background, skills, and experience, the expected annual salary range for this position is $200,000 - $400,000 USD + equity.\n\n - Visa sponsorship: We sponsor visas on a case-by-case basis.\n\n - Benefits: Inferact offers generous health, dental, and vision benefits as well as 401(k) company match.","salary_min":200000,"salary_max":400000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["gpu","llm","infrastructure","research"],"apply_url":"https://jobs.ashbyhq.com/inferact/3dee433c-7121-458c-8408-c193b6326ffb/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-08T20:04:02.323Z","expires_at":"2026-08-15T14:11:58.496586Z","created_at":"2026-07-09T14:11:07.184556Z","updated_at":"2026-07-16T14:11:58.62149Z","company_name":"Inferact","company_slug":"inferact","company_logo_url":"https://www.google.com/s2/favicons?domain=inferact.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/92eff494-6559-427b-8a18-9f3ed481a25a"},{"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":"b4864caf-5f0c-491e-bf2c-b88fbeea047b","company_id":"3da82454-107f-427f-88e7-01f315ef93fb","title":"Applied Research - RL \u0026 Agents","slug":"applied-research-rl-agents-13893686","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\nROLE IMPACT\n\nThis is a role at the intersection of cutting-edge RL/post-training methods and applied agent systems. You’ll have a direct impact on shaping how advanced models are aligned, deployed, and used in the real world by:\n\n - Advancing Agent Capabilities: Designing and iterating on next-generation AI agents that tackle real workloads—workflow automation, reasoning-intensive tasks, and decision-making at scale.\n\n - Building Robust Infrastructure: Developing the systems and frameworks that enable these agents to operate reliably, efficiently, and at massive scale.\n\n - Bridge Between Applications \u0026 Research: Translate ambiguous objectives into clear technical requirements that guide product and research priorities.\n\n - Prototype in the Field: Rapidly design and deploy agents, evals, and harnesses for real-world tasks to validate solutions.\n\n\n\n\nApplication-Driven Research \u0026 Infrastructure\n\n - Shape the direction and feature set for verifiers, the Environments Hub, training services, and other research platform offerings.\n\n - Build high‑quality examples, reference implementations, and “recipes” that make it easy for others to extend the stack.\n\n - Prototype agents and eval harnesses tailored to real-world use cases and external systems.\n\n - Pair with technical end‑users (research teams, infra‑heavy customers, open‑source contributors) to design environments, evals, and verifiers that reflect real workloads.\n\n\n\nPost-training \u0026 Reinforcement Learning\n\n - Design and implement novel RL and post-training methods (RLHF, RLVR, GRPO, etc.) to align large models with domain-specific tasks.\n\n - Build evaluations and harnesses and to measure reasoning, robustness, and agentic behavior in real-world workflows.\n\n - Prototype multi-agent and memory-augmented systems to expand capabilities for downstream applications.\n\n - Experiment with post-training recipes to optimize downstream performance.\n\n\n\nAgent Development \u0026 Infrastructure\n\n - Rapidly prototype and iterate on AI agents for automation, workflow orchestration, and decision-making.\n\n - Extend and integrate with agent frameworks to support evolving feature requests and performance requirements.\n\n - Architect and maintain distributed training/inference pipelines, ensuring scalability and cost efficiency.\n\n - Develop observability and monitoring (Prometheus, Grafana, tracing) to ensure reliability and performance in production deployments.\n\n\n\n\nREQUIREMENTS\n\n - Strong background in machine learning engineering, with experience in post-training, RL, or large-scale model alignment.\n\n - Experience with agent frameworks and tooling (e.g. DSPy, LangGraph, MCP, Stagehand).\n\n - Familiarity with distributed training/inference frameworks (e.g., vLLM, sglang, Accelerate, Ray, Torch).\n\n - Track record of research contributions (publications, open-source contributions, benchmarks) in ML/RL.\n\n - Passion for advancing the state-of-the-art in reasoning and building practical, agentic AI systems.\n\n - Strong technical writing abilities (documentation, blogs, papers) and research taste.\n\n - Eagerness to drive collaborations with external partners and engage with the broader open-source community.\n\n\n\n\nNICE-TO-HAVES\n\n - Experience with web programming (React, TypeScript, Next.js).\n\n - Experience running LLM evaluations and/or synthetic data generation.\n\n - Experience deploying containerized systems at scale (Docker, Kubernetes, Terraform).\n   \n\n\nWHAT WE OFFER\n\n - Cash Compensat","salary_min":150000,"salary_max":300000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["agents","distributed-systems","reinforcement-learning","llm","research"],"apply_url":"https://jobs.ashbyhq.com/PrimeIntellect/46d9d060-5f48-4491-848f-bafbeb3a4325/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-08T18:34:52.078Z","expires_at":"2026-08-15T14:10:47.454136Z","created_at":"2026-04-13T15:01:32.590705Z","updated_at":"2026-07-16T14:10:47.573529Z","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/b4864caf-5f0c-491e-bf2c-b88fbeea047b"},{"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":"9a7b6e3f-9ca5-4d74-8149-e52a00eeffdc","company_id":"3da82454-107f-427f-88e7-01f315ef93fb","title":"Applied Research - Evals \u0026 Data","slug":"applied-research-evals-data-2b9e0702","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\nRole Impact\n\nThis is a customer facing role at the intersection of cutting-edge RL/post-training methods, applied data, and agent systems. You’ll have a direct impact on shaping how advanced models are aligned, evaluated, deployed, and used in the real world by:\n\n - Advancing Agent Capabilities: Designing and iterating on next-generation AI agents that tackle real workloads—workflow automation, reasoning-intensive tasks, and decision-making at scale. Working with applied data from real deployments to continuously refine policies, improve reasoning, and enhance reliability and safety.\n\n - Building Robust Infrastructure: Developing the distributed systems, evaluation pipelines, and coordination frameworks that enable these agents to operate reliably, efficiently, and at massive scale. Building data capture, processing, and versioning workflows for feedback, model traces, and reward signals.\n\n - Bridge Between Customers \u0026 Research: Translating customer needs and insights from applied data into clear technical requirements that guide product and research priorities. Collaborating closely with RL and eval teams to ensure real-world signals inform model alignment and reward shaping.\n\n - Prototype in the Field: Rapidly designing and deploying agents, evals, and harnesses alongside customers to validate solutions. Using applied evaluation data to iterate on model performance and discover new capabilities.\n\n\nCustomer-Facing Engineering\n\n - Work side-by-side with customers to deeply understand workflows, data sources, and bottlenecks.\n\n - Prototype agents, data pipelines, and eval harnesses tailored to real use cases, then hand off hardened systems to core teams.\n\n - Translate customer insights and evaluation results into roadmap and research direction.\n\n\nPost-training \u0026 Reinforcement Learning\n\n - Design and implement novel RL and post-training methods (RLHF, RLVR, GRPO, etc.) to align large models with domain-specific tasks.\n\n - Build evaluation harnesses and verifiers to measure reasoning, robustness, and agentic behavior in real-world workflows.\n\n - Integrate applied data collection and analytics into the post-training process to surface regressions, emergent skills, and alignment opportunities.\n\n - Prototype multi-agent and memory-augmented systems to expand capabilities for customer-facing solutions.\n\n\nAgent Development \u0026 Infrastructure\n\n - Rapidly prototype and iterate on AI agents for automation, workflow orchestration, and decision-making.\n\n - Extend and integrate with agent frameworks to support evolving feature requests and performance requirements.\n\n - Architect and maintain distributed training and inference pipelines, ensuring scalability and cost efficiency.\n\n - Develop observability and monitoring (Prometheus, Grafana, tracing) to ensure reliability and performance in production deployments.\n\n\nRequirements\n\n - Strong background in machine learning engineering, with experience in post-training, RL, or large-scale model alignment.\n\n - Experience with applied data workflows and evaluation frameworks for large models or agents (e.g., SWE-Bench, HELM, EvalFlow, internal eval pipelines).\n\n - Deep expertise in distributed training/inference frameworks (e.g., vLLM, sglang, Ray, Accelerate).\n\n - Experience deploying containerized systems at scale (Docker, Kubernetes, Terraform).\n\n - Track record of research contributions (publications, open-source contributions, benchmarks) in ML/RL.\n\n - Passion for advancing the ","salary_min":150000,"salary_max":300000,"location":"San Francisco, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"senior","tags":["reinforcement-learning","agents","data-pipeline","distributed-systems","llm","research","evaluation"],"apply_url":"https://jobs.ashbyhq.com/PrimeIntellect/bbfe94a6-d1a8-47e9-86af-f117277cdacb/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-08T18:34:09.743Z","expires_at":"2026-08-15T14:10:47.08875Z","created_at":"2026-04-13T15:01:32.581029Z","updated_at":"2026-07-16T14:10:47.245952Z","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/9a7b6e3f-9ca5-4d74-8149-e52a00eeffdc"},{"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. 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 benefits through Carrot\n\n - Complimentary in-office lunches and snacks provided\n\n - Access to state-of-the-art AI models, generous usage o","salary_min":150000,"salary_max":250000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["agents","healthcare","search","research"],"apply_url":"https://jobs.ashbyhq.com/distyl/462996c2-f91d-4f21-bf77-7215a0be3d73/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-07T15:39:15.352Z","expires_at":"2026-08-15T14:19:19.782973Z","created_at":"2026-06-28T14:17:23.752319Z","updated_at":"2026-07-16T14:19:19.904976Z","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/8f723451-d172-49ec-8f31-083cc8165ebe"},{"id":"e880b7b3-5c24-4ddd-8769-b56233d39c69","company_id":"aa372131-86ce-432a-af45-e2b42a79ba29","title":"Research Engineers, Data","slug":"research-engineers-data-33bc4535","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 build data systems that power reliable AI workflows across enterprise environments\n\n - Develop pipelines for collecting, cleaning, transforming, labeling, and evaluating domain-specific data used by AI systems\n\n - Create data quality frameworks that identify coverage gaps, ambiguity, drift, duplication, leakage, and other failure modes\n\n - Build tools and workflows that help teams turn raw customer data into usable context for retrieval, evaluation, reasoning, and execution\n\n - Partner with AI Researchers and AI Engineers to understand how data quality affects system behavior and production outcomes\n\n - Develop synthetic data, annotation, and feedback-loop strategies to improve system performance in areas where real-world data is sparse or noisy\n\n - Analyze customer workflows and datasets to determine what information AI systems need, where that information should come from, and how it should be represented\n\n - Communicate clearly with internal teams and customer stakeholders about data assumptions, limitations, risks, and tradeoffs\n\n\n\n\nWHO YOU ARE\n\n - Experience Building Data Systems for AI: You have built data pipelines, evaluation datasets, labeling workflows, retrieval corpora, or similar systems that improve model or agent behavior\n\n - Strong Data Engineering Fundamentals: You write clean Python and SQL, understand data modeling and pipeline reliability, and can build systems that are maintainable under production constraints\n\n - Research-Oriented Builder: You are comfortable investigating how data quality, structure, and representation affect AI system performance\n\n - AI-Native Working Style: You use AI tools daily to accelerate coding, analysis, debugging, exploration, and workflow automation\n\n - Comfort with Ambiguous Data: You can reason through messy enterprise datasets, incomplete documentation, conflicting business definitions, and changing requirements\n\n - Bias Towards Measurement: You prefer to make data quality and system behavior observable through concrete metrics, evaluations, and experiments\n\n - Customer Environment Readiness: You can work directly with customer teams to understand their data, ask precise questions, and explain tradeoffs clearly\n\n - Ownership Mentality: You take responsibility for whether the data layer enables the AI system to deliver reliable value 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. 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","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","search","data-pipeline","fine-tuning","research"],"apply_url":"https://jobs.ashbyhq.com/distyl/fe44469c-0f27-408c-878c-5e296b7db50e/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-07T15:39:12.519Z","expires_at":"2026-08-15T14:19:19.610383Z","created_at":"2026-06-28T14:17:24.85961Z","updated_at":"2026-07-16T14:19:19.741508Z","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/e880b7b3-5c24-4ddd-8769-b56233d39c69"},{"id":"5d222f90-90c4-4238-b68f-02a7bee00eaf","company_id":"a0000000-0000-0000-0000-000000000001","title":"Engineering Manager, Research Data Platform","slug":"engineering-manager-research-data-platform-40a3e0af","description":"About Anthropic \n Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n About the role\n Anthropic's researchers generate and depend on enormous amounts of data — training runs, evaluations, RL transcripts, annotations etc... The Research Data Platform team builds the systems that make that data easy to produce, find, query, and trust. We work in two modes: we build  platform components that other systems plug into (for example, a metrics library that training frameworks integrate to record and retrieve run data), and we own core datasets end to end (for example, the data pipeline behind RL transcripts).\n As the team's tech lead, your job starts with our users. You'll work directly with researchers — and with the engineers who support them — to understand how they actually work, where managing data slows them down, and where a well-built platform component or a well-curated dataset would change what's possible. You'll turn what you learn into technical direction for the team, in partnership with the team's manager, who owns priorities and people. A central ambition you'll drive: a small set of canonical, well-documented datasets — starting with the core data model for RL — that researchers trust and standardize on, rather than every team managing its own copies.\n You'll spend your first few months close to the code and close to users: shipping improvements in our core systems, embedding with research teams, and building your own map of their workflows. As the team grows, this role has a natural path into formal people leadership for someone who wants it.\n Responsibilities\n \n Work directly with researchers and the engineers supporting them to understand their workflows, identify the highest-leverage opportunities, and shape what the team builds next\n Set the technical direction for the team across our platform and our datasets\n Design and build platform components that other teams plug into — libraries, services, and interfaces such as the metrics library used by training frameworks\n Own core datasets end to end: the pipelines that produce them, the schemas that define them, and the documentation and guarantees that make researchers trust them\n Drive convergence toward canonical datasets — including the core data model for RL transcripts — that research teams standardize on\n Lead complex, multi-quarter projects that span several systems and teams, staying hands-on in the code\n Raise the team's technical bar through design reviews, mentorship, and the quality of your own work\n \n You may be a good fit if you:\n \n Have built and operated data-intensive systems at scale — pipelines, storage layers, query systems — with strong instincts for data modeling and schema design that hold up as usage grows\n Have set technical direction for a team, or owned the architecture of a data platform that other teams build on\n Treat internal users as customers: you do the discovery work, iterate with users, and measure success by adoption rather than by shipping\n Understand that researchers aren’t typical internal customers — the work is exploratory by nature, workflows differ from team to team, and requirements are discovered through experiments rather than specified up front\n Can build for that motion — keeping interfaces stable and data trustworthy while use cases change underneath you, and judging when a quick, disposable solution serves research better than a durable one\n Lead through influence — aligning engineers and stakeholders without relying on formal authority\n Are results-oriented and pragmatic, willing to do unglamorous work when it's the highest-leverage thing\n Are excited about learning the fundamentals of machine learning research (deep ML expertise is not required)\n Care about the societal impacts of your work\n \n Strong candidates may also have\n \n Experience with large-scale ETL and columnar or analytical storage (e.g., Spark, BigQuery, ClickHouse, DuckDB, Parquet)\n Experience with metrics or experiment-tracking systems, or high-volume time-series data\n Experience with dataset management, cataloging, or lineage tooling\n Built developer tooling or internal data platforms for demanding technical users — including in domains like quantitative trading, where fast-moving, exploratory data work looks a lot like research\n A working knowledge of machine learning\n Worked in, or closely with, an ML research lab\n Interest in — or experience with — people management and growing engineers\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","salary_min":405000,"salary_max":850000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["fine-tuning","alignment","data-pipeline","research"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5297059008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-07T02:44:47Z","expires_at":"2026-08-15T14:00:21.163892Z","created_at":"2026-07-07T14:00:21.432276Z","updated_at":"2026-07-16T14:00:21.279765Z","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/5d222f90-90c4-4238-b68f-02a7bee00eaf"},{"id":"6e73bc75-a490-4b93-af2d-5d0040a7eb71","company_id":"6ea0f41a-b13e-481a-b410-5195f391f939","title":"Research Engineer, Post-Training Inference","slug":"research-engineer-post-training-inference-ff4ae18b","description":"About the role \n The Model Shaping team at Together AI works on products and research focused on tailoring open foundation models to downstream applications. We build services that enable machine learning developers to choose the best models for their tasks and further improve these models using domain-specific data. In addition, we develop new methods for more efficient model training and evaluation, drawing inspiration from a broad range of ideas across machine learning, natural language processing, and ML systems.\n As a Research Engineer within Model Shaping, you will develop a platform that enables users to customize open-source models with their own data. Working across the training and inference stacks, you will build and improve our Fine-Tuning, Reinforcement Learning, and Evaluation services – from ensuring a seamless path from post-training to production serving, to optimizing the inference engine for RL training workloads. You will collaborate closely with our product, research, and engineering teams to keep the API reliable, performant, and well integrated into the company's technical infrastructure. Above all, you will help build the foundational layer of the open-source AI ecosystem, enabling developers around the world to efficiently create high-quality models tailored to their specific applications.\n Responsibilities \n \n Design and build Together’s systems for customizing open-source models\n Build integrations between the Model Shaping and Inference platforms to ensure a seamless path from post-training to serving production workloads\n Add features to inference engines for large-scale post-training experiments, including optimizations for RL workloads\n Make sure the service is stable and robust, participating in an on-call rotation and ensuring 24/7 availability of our platform\n \n Requirements \n \n Have 2+ years of experience building and deploying machine learning-based services in a production environment\n Have hands-on experience with modern inference engines, such as SGLang, vLLM, and TensorRT-LLM\n Are familiar with the latest methods for fine-tuning LLMs and other AI models\n Have a strong software engineering background in Python or Go\n Stay up to date with the latest advances and trends in the machine learning community\n \n Experience in any of the following will make you stand out \n \n Serving low-precision (FP4/FP8) models, multiple LoRA adapters within one model instance (Multi-LoRA), or models distributed across several GPU nodes\n Optimizing the performance of RL training workloads\n Developing CUDA/Triton/CuTE DSL kernels for inference\n Developing large-scale and high-load production systems\n Maintaining or contributing to open-source ML projects\n Managing machine learning workloads on Kubernetes clusters\n \n About Together AI \n Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, ATLAS, RedPajama, and Mamba. We invite you to join a passionate group of researchers in our journey in building the next generation AI infrastructure.\n Compensation \n We offer competitive compensation, startup equity, health insurance, and other benefits. The US base salary range for this full-time position is $200,000 - $290,000. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.\n Equal Opportunity \n Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more.\n Please see our privacy policy at  https://www.together.ai/privacy","salary_min":200000,"salary_max":290000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["search","llm","nlp","gpu","reinforcement-learning","fine-tuning","generative-ai","research"],"apply_url":"https://job-boards.greenhouse.io/togetherai/jobs/5179372007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-06T18:21:40Z","expires_at":"2026-08-15T14:02:19.341947Z","created_at":"2026-07-09T14:02:08.323229Z","updated_at":"2026-07-16T14:02:19.456183Z","company_name":"Together AI","company_slug":"together-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=together.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/6e73bc75-a490-4b93-af2d-5d0040a7eb71"},{"id":"e5f37346-7318-4d03-bba6-98582a3995f9","company_id":"3d233526-89a8-48ea-b0ed-3304a35b8acf","title":"Senior Machine Learning Engineer, Health","slug":"senior-machine-learning-engineer-health-32b476c8","description":"WHOOP is an advanced health and fitness wearable, on a mission to unlock human performance. WHOOP empowers its members to improve their health and perform at a higher level by providing a deep understanding of their bodies and daily lives.\nThe Health team is responsible for developing novel algorithms and features that expand our health sensing capabilities. Our work spans several key areas, including women’s health, software as a medical device, wellness monitoring, longevity research, and emerging health insights. We combine continuous physiological data with clinical research and expert knowledge to generate features that are both scientifically grounded and deeply impactful for members.\nAs a SeniorMachine Learning Engineer on our Health team, you will design, build, and productionize ML systems that deliver meaningful, personalized health metrics to millions of members. You will work at the intersection of data science, backend engineering, and cloud infrastructure—deploying robust, scalable, and reliable ML solutions built on physiological and behavioral data streams. This role emphasizes strong coding skills, system design, and the ability to deliver production-ready ML services.\n","salary_min":150000,"salary_max":210000,"location":"Boston, MA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["healthcare","cloud","machine-learning","research"],"apply_url":"https://jobs.lever.co/whoop/d71ade70-3925-4dee-bf5c-8804b3897914/apply","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-06T16:29:28.426Z","expires_at":"2026-08-15T14:17:40.907212Z","created_at":"2026-07-09T14:16:34.500559Z","updated_at":"2026-07-16T14:17:41.027765Z","company_name":"WHOOP","company_slug":"whoop","company_logo_url":"https://www.google.com/s2/favicons?domain=whoop.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e5f37346-7318-4d03-bba6-98582a3995f9"}],"market_demand_pack":{"amount_cents":2900,"api_checkout_url":"https://aidevboard.com/api/v1/checkout?product_id=aidevboard_ai_skills_demand_pack","checkout_url":"https://aidevboard.com/market-demand-pack?qc=api-jobs-market-demand-pack\u0026utm_campaign=skills_demand_pack\u0026utm_medium=jobs_api\u0026utm_source=api","currency":"USD","description":"Full ranked public AI/ML demand CSV, source job URLs, and decision brief with market and offer angles.","fulfillment":"automatic_email_after_paid_checkout","human_checkout_url":"https://aidevboard.com/market-demand-pack?qc=api-jobs-market-demand-pack\u0026utm_campaign=skills_demand_pack\u0026utm_medium=jobs_api\u0026utm_source=api","name":"AI Market Demand Pack","next_step":"Open checkout_url for Stripe Checkout, or call api_checkout_url to get the non-charging checkout handoff payload.","price_usd":29,"product_id":"aidevboard_ai_skills_demand_pack","quote_url":"https://aidevboard.com/api/v1/quote?product_id=aidevboard_ai_skills_demand_pack"},"page":1,"per_page":20,"total":763,"total_pages":39}