{"access":{"catalog_url":"https://aidevboard.com/api/v1/catalog","description":"Public read endpoints are open and free. 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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\nAbout the Role\n\nWe're looking for an Engineering Manager to help scale a world-class, senior-heavy engineering organization working at the frontier of AI inference. You will lead a team of exceptional engineers across areas such as inference systems, performance, kernels, accelerators, and cloud infrastructure while creating the management capacity required for Inferact to keep growing quickly without sacrificing technical quality or execution speed.\n\nThis is a deeply technical management role. You will own team execution, hiring, coaching, prioritization, and operating rhythm, while staying close enough to the work to earn the trust of engineers solving hard systems problems. You will partner closely with engineering and company leadership to turn company priorities into clear technical direction, remove blockers, improve how the team works, and help every engineer do the highest-leverage work of their career.\n\n\n\nSkills and Qualifications\n\nMinimum qualifications:\n\n - Bachelor's degree or equivalent experience in computer science, engineering, or similar.\n\n - Demonstrated experience managing strong software engineers in technically demanding systems, infrastructure, ML systems, distributed systems, or performance-oriented environments.\n\n - Strong technical credibility with the ability to understand architecture, ask useful questions, challenge assumptions, and help unblock senior engineers without becoming a bottleneck or overriding domain experts.\n\n - Experience owning the full people-management lifecycle, including hiring, onboarding, 1:1s, feedback, performance management, career development, team health, and retention.\n\n - Ability to translate ambiguous company priorities into clear team goals, sequencing, ownership, and execution plans while maintaining urgency in a fast-changing startup environment.\n\n - Track record of building effective engineering operating rhythms around planning, decision-making, technical reviews, cross-team coordination, and accountability without creating unnecessary process.\n\n - Strong judgment about when to dive into a technical problem, when to delegate, and how to create leverage for senior engineers rather than competing with them for ownership.\n\n - 2+ years of management experience with 10+ reports.\n\nPreferred qualifications:\n\n - Experience leading teams working on AI / ML infrastructure, model serving, distributed systems, GPU infrastructure, compilers, performance engineering, cloud infrastructure, or developer platforms.\n\n - Experience managing senior, staff, principal, research-adjacent, or PhD engineers in an environment where technical credibility and autonomy are especially important.\n\n - Experience scaling an engineering team through rapid hiring, including interviewer calibration, hiring decisions, onboarding systems, and thoughtful team design.\n\n - Familiarity with inference systems and adjacent technologies such as vLLM, SGLang, PyTorch, CUDA / Triton, Kubernetes, distributed serving, or accelerator infrastructure.\n\n - Experience partnering closely with founders or engineering executives in an early-stage company where priorities, team boundaries, and organizational needs evolve quickly.\n\nBonus points if you have:\n\n - Built or scaled a small, unusually high-talent engineering team where the technical surface area was specialized and the quality bar was extremely high.\n\n - Previously remained hands-on as an engineer or technical lead before moving into management and can still reason credibly about complex production systems.\n\n - Managed teams spanning multiple technical specialties, geographies, or time zones while preserving strong ownership and communication.\n\n - Helped establish engineering management practices from an early stage rather than inheriting a mature management system.\n\n - Contributed to open-source infrastructure, AI systems, developer tools, or technically ambitious products used by external engineers.\n\nLogistics\n\n - Location: This role is based in San Francisco, California and is in-office.\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":"junior","tags":["cloud","llm","pytorch","gpu","mlops","distributed-systems","research"],"apply_url":"https://jobs.ashbyhq.com/inferact/98837c38-33a9-4085-97e7-1974dcf234a1/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-14T11:23:31.216Z","expires_at":"2026-11-13T16:58:50.529662Z","created_at":"2026-10-14T15:22:31.292734Z","updated_at":"2026-10-14T16:58:50.533097Z","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/19f2bcb8-dfcf-45a4-b0a7-855bfd707cfa"},{"id":"1d5ba28a-37f9-4138-9c77-647324c5d234","company_id":"b6e5a3d1-9bde-4a82-8d78-9f38ed99ee81","title":"Research Manager - Post-Training for Agents","slug":"research-manager-post-training-for-agents-5de82862","description":"As a Research Manager within Datadog AI Research (DAIR), you will lead a team working on fundamental research in post-training and autonomous agents while remaining deeply engaged in the technical direction of the work. You will advance reinforcement learning, simulated environments, synthetic data generation, and evaluation methods for agents operating across complex real-world systems. You will provide technical leadership to a team of researchers and research engineers, mentor and develop highly technical talent, shape research direction, and collaborate with Datadog’s product and engineering teams to translate research advances into products. This role is designed for an exceptional researcher or technical leader who wants to maintain a strong hands-on research identity while developing and leading a high-performing team.  \n At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them.  \n What You'll Do:  \n \n Lead research in agent post-training and reinforcement learning, setting the technical direction for ambitious research programs grounded in observability and security\n Guide and mentor researchers and research engineers while remaining closely involved in research strategy, experimentation, model development, and technical problem-solving \n Design and advance reinforcement learning approaches, post-training methods, and training loops for agents operating in complex environments \n Build simulated environments, synthetic data generation approaches, and evaluation frameworks that enable scalable agent training and rigorous measurement\n Collaborate with cross-functional teams across Research, Product, and Engineering to translate advances in autonomous agents into scalable Datadog capabilities\n Contribute to research publications, present at top-tier conferences such as NeurIPS, ICLR, and ICML, and help open-source key model artifacts and benchmarks \n \n Who You Are:  \n \n You hold a PhD in Computer Science, Machine Learning, or a related field, or have equivalent experience, with deep expertise in areas such as reinforcement learning, AI agents, post-training, or generative modeling \n You have demonstrated the ability to lead technically ambitious research at meaningful scale, whether in an industry research lab, startup, academic environment, or another research setting \n You have extensive hands-on experience designing or implementing reinforcement learning systems, agent training methods, simulated environments, synthetic data pipelines, or evaluation frameworks\n You have a track record of research impact through influential publications, significant model or system contributions, widely used research artifacts, or equivalent technical achievements \n You have operated at a senior technical level and have experience leading, mentoring, and developing teams of researchers or engineers, while remaining deeply hands-on in technical direction and execution \n You want to develop and support a research team while maintaining a strong technical identity, and you can communicate complex research findings effectively across technical and non-technical audiences \n \n Datadog values people from all walks of life. We know not everyone will meet all the above qualifications on day one. That’s okay. If you’re passionate about technology and want to grow your experience, we encourage you to apply.  \n Benefits and Growth:  \n \n Competitive global benefits \n New hire stock equity (RSUs) and employee stock purchase plan (ESPP)\n Opportunity to collaborate closely with colleagues across the Datadog offices in New York City and Paris \n Opportunity to attend and present at conferences and meetups \n Intra-departmental mentor and buddy program for in-house networking\n An inclusive company culture, ability to join our Community Guilds (Datadog employee resource groups)  \n \n Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog. \n #LI-Hybrid\n Datadog offers a competitive salary and equity package, and may include variable compensation. Actual compensation is based on factors such as the candidate's skills, qualifications, and experience. In addition, Datadog offers a wide range of best in class, comprehensive and inclusive employee benefits for this role including healthcare, dental, parental planning, and mental health benefits, a 401(k) plan and match, paid time off, fitness reimbursements, and a discounted employee stock purchase plan.\n The reasonably estimated yearly salary for this role at Datadog is:\n $244,000 — $305,000 USD \n \n About Datadog:  \n Datadog is the leading observability and security platform for the AI era, providing businesses with unified visibility across the technology stack to manage complexity at scale. It brings applic","salary_min":244000,"salary_max":305000,"location":"New York, NY","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["reinforcement-learning","search","agents","healthcare","data-pipeline","research"],"apply_url":"https://careers.datadoghq.com/detail/8279173/?gh_jid=8279173","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T17:57:23Z","expires_at":"2026-11-13T16:55:38.246957Z","created_at":"2026-10-14T15:19:28.467785Z","updated_at":"2026-10-14T16:55:38.249824Z","company_name":"Datadog","company_slug":"datadog","company_logo_url":"https://www.google.com/s2/favicons?domain=datadoghq.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/1d5ba28a-37f9-4138-9c77-647324c5d234"},{"id":"0e12d082-413f-4951-a181-79c2db46f814","company_id":"75f38274-c5f0-417d-92f4-bd42624e749e","title":"Senior UX Researcher","slug":"senior-ux-researcher-1708bb24","description":"Intrinsic is an AI robotics group at Google aiming to reimagine the potential of industrial robotics. Our team believes that advances in AI, perception and simulation will redefine what’s possible for industrial robotics in the near future – with software and data at the core. \n Our mission is to make industrial robotics intelligent, accessible, and usable for millions more businesses, entrepreneurs, and developers. We are a dynamic team of engineers, roboticists, designers, and technologists who are passionate about unlocking the creative and economic potential of industrial robotics. \n Role\n As a UX Researcher at Intrinsic, you will lead end-to-end user research to shape the developer tools, operational interfaces, and platform capabilities that enable intelligent industrial robotics in manufacturing and factory environments.  Working as part of an embedded product research team inside Intrinsic, you will partner closely with product managers, engineers, and industrial designers to turn complex technical workflows into intuitive, efficient human-machine experiences for robotics developers, system integrators, machine operators, technicians, and plant managers.\n How your work moves the mission forward\n \n Plan and execute generative and evaluative research studies across factory UI, developer tools, and robotic platform interfaces for target users including robotics application developers, system integrators, machine operators, technicians, and plant managers.\n Translate complex field observations and technical developer feedback into clear, actionable design recommendations.\n Partner with product and engineering leads to align UX research goals with platform engineering milestones.\n Conduct field work and observational studies on-site in industrial and manufacturing plant environments, including up to 15-20% domestic and international travel.\n Flex into product management or program management activities when required to maintain project momentum and align team execution.\n \n Skills you will need to be successful\n \n Master's or PhD degree in Human-Computer Interaction (HCI), Human-Machine Interaction (HMI), Cognitive Science, Psychology, Computer Science, or equivalent practical experience.\n 4+ years of user research experience in developer products, complex software platforms, or hardware/robotics interfaces.\n Proven experience operating at a startup-like pace within an enterprise setting (such as Alphabet/Google), with the agility to flex into product management or program management support to accelerate roadmap execution.\n Experience leveraging modern AI and agentic tools (e.g., Gemini, NotebookLM, automated synthesis tools) to streamline research synthesis and workflows while maintaining rigorous strategic human oversight.\n Demonstrated expertise in qualitative research methods (ethnography, field studies, usability testing, interviewing).\n Willingness to conduct field research in active industrial, manufacturing, and factory environments (requiring standard PPE and safety protocol compliance).\n \n Skills that will differentiate your candidacy\n \n Experience conducting user research in industrial or manufacturing environments.\n Experience with complex software engineering interfaces such as SDKs, APIs, or AI-human interactions.\n Familiarity with mixed-methods research, combining qualitative insights with quantitative usage telemetry.\n Strong track record of influencing engineering roadmap decisions through user insights.\n \n  \n The base salary for this full-time position is between $159,200 - $217,300 (USD) + 15% bonus + equity + benefits . Your recruiter will share more about the specific salary range for your targeted location during the hiring process. \n At Intrinsic, we are proud to be an equal opportunity workplace. Employment at Intrinsic is based solely on a person's merit and qualifications directly related to professional competence. Intrinsic does not discriminate against any employee or applicant because of race, creed, color, religion, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition (including breastfeeding), or any other basis protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. It is Intrinsic’s policy to comply with all applicable national, state and local laws pertaining to nondiscrimination and equal opportunity.\n If you have a disability or special need that requires accommodation, please contact us at: candidate-support@intrinsic.ai.","salary_min":159200,"salary_max":217300,"location":"Mountain View, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["robotics","cloud","agents","research"],"apply_url":"https://boards.greenhouse.io/intrinsicrobotics/jobs/6219429004?gh_jid=6219429004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-08T04:52:11Z","expires_at":"2026-11-13T16:57:19.656422Z","created_at":"2026-10-14T15:21:04.908818Z","updated_at":"2026-10-14T16:57:19.659167Z","company_name":"Intrinsic","company_slug":"intrinsic","company_logo_url":"https://www.google.com/s2/favicons?domain=intrinsic.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/0e12d082-413f-4951-a181-79c2db46f814"},{"id":"84267438-bcb6-4a44-97ac-4f18bdb2ad77","company_id":"a0000000-0000-0000-0000-000000000001","title":"Staff+ Software Engineer, Research Systems Engineering","slug":"staff-software-engineer-research-systems-engineering-27aecffe","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 Infrastructure organization builds and operates the distributed systems that train, serve, and secure our AI models. Every other team at Anthropic depends on these systems.\n In this role, you'll work directly with our Research teams to build the reliable, scalable, performant infrastructure our research depends on. This is a cross-functional systems team: you'll scope and lead complex, multi-month infrastructure projects and resolve the performance and scalability bottlenecks that limit how fast we can grow.\n Key responsibilities\n \n Independently scope and lead complex, multi-month infrastructure projects, from an ambiguous starting point through to a production system\n Build deep partnerships with researchers and Research teams to understand their needs and deliver for them\n Mentor other engineers and help raise the technical bar for the team\n Build alignment on technical direction across multiple teams, working through ambiguous problem spaces\n Take ownership of the reliability, scalability, and security of the systems you build as usage and complexity grow\n Lead the improvement of operational processes across Infrastructure, such as incident response, postmortems, and on-call rotations, so the team learns from every incident\n \n Minimum qualifications\n \n Experience designing, building, and operating large-scale distributed systems or infrastructure in production\n Experience independently scoping and delivering complex, ambiguous, multi-month technical projects\n Prior experience as a technical lead or mentor for other engineers\n Experience making architectural decisions that other engineers and teams build on top of\n Strong software engineering fundamentals and proficiency in at least one programming language (e.g., Python, Rust, Go, Java)\n Experience with modern cloud infrastructure (e.g., Kubernetes, infrastructure as code, AWS, GCP)\n Strong written and verbal communication skills, with experience building alignment across multiple teams or stakeholders\n \n Preferred qualifications\n \n 10+ years of software engineering experience, not including internships\n Experience with machine learning infrastructure (e.g., GPUs, TPUs, Trainium) and associated networking infrastructure (e.g., NCCL)\n Low-level systems experience (e.g., Linux kernel tuning, eBPF)\n Experience applying security or privacy engineering best practices\n The annual compensation range for this role is listed below. \n For sales roles, the range provided is the role’s On Target Earnings (\"OTE\") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\n Annual Salary:\n $320,000 — $485,000 USD \n Logistics \n Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\n Required field of study:  A field relevant to the role as demonstrated through coursework, training, or professional experience\n Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position\n Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.\n Visa sponsorship:  We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.\n We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're eve","salary_min":320000,"salary_max":485000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["distributed-systems","alignment","cloud","research","infrastructure"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5447118008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-07T20:26:11Z","expires_at":"2026-11-13T16:54:56.973202Z","created_at":"2026-10-14T15:17:23.460298Z","updated_at":"2026-10-14T16:54:56.975982Z","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/84267438-bcb6-4a44-97ac-4f18bdb2ad77"},{"id":"774984d7-9daf-4940-96bf-f4a93cc9d946","company_id":"b6e5a3d1-9bde-4a82-8d78-9f38ed99ee81","title":"Staff Research Scientist - Post-Training for Agents","slug":"staff-research-scientist-post-training-for-agents-2ad2fda2","description":"As a Staff Research Scientist within Datadog AI Research (DAIR), you will drive research in post-training and autonomous agents as a hands-on individual contributor. You will advance reinforcement learning, simulated environments, synthetic data generation, and evaluation methods for agents operating across complex real-world systems. You will set the technical direction for ambitious research programs, raise the technical bar for the researchers and research engineers around you, and collaborate with Datadog's product and engineering teams to translate research advances into products. \n  \n What You'll Do: \n \n Drive research in agent post-training and reinforcement learning, shaping the technical direction of ambitious research programs grounded in observability and security\n Own research problems end to end, from framing the question through experimentation, model development, and evaluation\n Design and advance reinforcement learning approaches, post-training methods, and training loops for agents operating in complex environments\n Build simulated environments, synthetic data generation approaches, and evaluation frameworks that enable scalable agent training and rigorous measurement\n Raise the technical bar across the team by reviewing research directions, mentoring researchers and research engineers, and setting standards for experimental rigor\n Collaborate with cross-functional teams across Research, Product, and Engineering to translate advances in autonomous agents into scalable Datadog capabilities\n Contribute to research publications, present at top-tier conferences such as NeurIPS, ICLR, and ICML, and help open-source key model artifacts and benchmarks\n \n  \n Who You Are: \n \n You hold a PhD in Computer Science, Machine Learning, or a related field, or have equivalent experience, with deep expertise in areas such as reinforcement learning, AI agents, post-training, or generative modeling\n You have driven technically ambitious research at meaningful scale as an individual contributor, whether in an industry research lab, startup, academic environment, or another research setting\n You have extensive hands-on experience designing and implementing reinforcement learning systems, agent training methods, simulated environments, synthetic data pipelines, or evaluation frameworks\n You have a track record of research impact through influential publications, significant model or system contributions, widely used research artifacts, or equivalent technical achievements\n You set technical direction through influence rather than authority, and you have mentored other researchers or engineers and elevated the quality of work around you\n You want to stay deeply hands-on in research for the long term, and you can communicate complex research findings effectively across technical and non-technical audiences\n \n  \n Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog. \n  \n About Datadog:  \n Datadog (NASDAQ: DDOG) is a global SaaS business, delivering a rare combination of growth and profitability. We are on a mission to break down silos and solve complexity in the cloud age by enabling digital transformation, cloud migration, and infrastructure monitoring of our customers’ entire technology stacks. Built by engineers, for engineers, Datadog is used by organizations of all sizes across a wide range of industries. Together, we champion professional development, diversity of thought, innovation, and work excellence to empower continuous growth. Join the pack and become part of a collaborative, pragmatic, and thoughtful people-first community where we solve tough problems, take smart risks, and celebrate one another. Learn more about #DatadogLife on Instagram, LinkedIn, and Datadog Learning Center.\n  \n Equal Opportunity at Datadog: \n Datadog is an Affirmative Action and 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. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. Here are our Candidate Legal Notices for your reference.\n  \n Your Privacy: \n Any information you submit to Datadog as part of your application will be processed in accordance with Datadog’s Applicant and Candidate Privacy Notice.\n  \n Datadog offers a competitive salary and equity package, and may include variable compensation. Actual compensation is based on factors such as the candidate's skills, qualifications, and experience. In addition, Datadog offers a wide range of best in class, comprehensive and inclusive employee benefits for this role including healthcare, dental, parental planning, and mental health benefits, a 401(k) plan and match, paid time off, fitness reimbursements, and a discounted ","salary_min":320000,"salary_max":400000,"location":"New York, NY","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["healthcare","reinforcement-learning","search","data-pipeline","agents","research"],"apply_url":"https://careers.datadoghq.com/detail/8261934/?gh_jid=8261934","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-07T16:34:22Z","expires_at":"2026-11-13T16:55:38.307935Z","created_at":"2026-10-14T15:19:28.526513Z","updated_at":"2026-10-14T16:55:38.310977Z","company_name":"Datadog","company_slug":"datadog","company_logo_url":"https://www.google.com/s2/favicons?domain=datadoghq.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/774984d7-9daf-4940-96bf-f4a93cc9d946"},{"id":"bed4ba83-1003-483b-ae16-2e373644bf3e","company_id":"b6e5a3d1-9bde-4a82-8d78-9f38ed99ee81","title":"Staff Research Scientist - Foundation \u0026 World Models ","slug":"staff-research-scientist-foundation-world-models-95a79182","description":"As a Staff Research Scientist within Datadog AI Research (DAIR), you will drive research in foundation models and world models as a hands-on individual contributor. You will advance large-scale pre-training and multimodal learning across the diverse signals generated by distributed systems, including metrics, traces, logs, topology, and events. You will set the technical direction for ambitious research programs, raise the technical bar for the researchers and research engineers around you, and collaborate with Datadog's product and engineering teams to translate research advances into products. \n  \n What You'll Do: \n \n Drive research in foundation models, world models, and multimodal learning, shaping the technical direction of ambitious research programs grounded in observability\n Own research problems end to end, from framing the question through experimentation, model development, and evaluation\n Train large-scale multimodal models on diverse telemetry data, including metrics, logs, traces, topology, events, and other non-text modalities\n Advance approaches to pre-training, representation learning, world modeling, scaling, and evaluation for models that learn the dynamics of complex distributed systems\n Raise the technical bar across the team by reviewing research directions, mentoring researchers and research engineers, and setting standards for experimental rigor\n Collaborate with cross-functional teams across Research, Product, and Engineering to translate research advances into scalable Datadog capabilities\n Contribute to research publications, present at top-tier conferences such as NeurIPS, ICLR, and ICML, and help open-source key model artifacts and benchmarks\n \n  \n Who You Are: \n \n You hold a PhD in Computer Science, Machine Learning, or a related field, or have equivalent experience, with deep expertise in areas such as foundation models, world models, multimodal learning, or generative modeling\n You have driven technically ambitious research at meaningful scale as an individual contributor, whether in an industry research lab, startup, academic environment, or another research setting\n You have extensive hands-on experience designing, training, and evaluating large-scale deep learning models (such as large language models), with experience in multimodal or non-text data considered a strong plus\n You have a track record of research impact through influential publications, significant model or system contributions, widely used research artifacts, or equivalent technical achievements\n You set technical direction through influence rather than authority, and you have mentored other researchers or engineers and elevated the quality of work around you\n You want to stay deeply hands-on in research for the long term, and you can communicate complex research findings effectively across technical and non-technical audiences\n \n  \n Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog. \n  \n About Datadog:  \n Datadog (NASDAQ: DDOG) is a global SaaS business, delivering a rare combination of growth and profitability. We are on a mission to break down silos and solve complexity in the cloud age by enabling digital transformation, cloud migration, and infrastructure monitoring of our customers’ entire technology stacks. Built by engineers, for engineers, Datadog is used by organizations of all sizes across a wide range of industries. Together, we champion professional development, diversity of thought, innovation, and work excellence to empower continuous growth. Join the pack and become part of a collaborative, pragmatic, and thoughtful people-first community where we solve tough problems, take smart risks, and celebrate one another. Learn more about #DatadogLife on Instagram, LinkedIn, and Datadog Learning Center.\n  \n Equal Opportunity at Datadog: \n Datadog is an Affirmative Action and 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. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. Here are our Candidate Legal Notices for your reference.\n  \n Your Privacy: \n Any information you submit to Datadog as part of your application will be processed in accordance with Datadog’s Applicant and Candidate Privacy Notice.\n  \n Datadog offers a competitive salary and equity package, and may include variable compensation. Actual compensation is based on factors such as the candidate's skills, qualifications, and experience. In addition, Datadog offers a wide range of best in class, comprehensive and inclusive employee benefits for this role including healthcare, dental, parental planning, and mental health benefits, a 401(k) plan and match, paid time off, fitness rei","salary_min":320000,"salary_max":400000,"location":"New York, NY","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["distributed-systems","healthcare","deep-learning","generative-ai","pre-training","search","llm","research"],"apply_url":"https://careers.datadoghq.com/detail/8261937/?gh_jid=8261937","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-07T16:34:19Z","expires_at":"2026-11-13T16:55:38.303397Z","created_at":"2026-10-14T15:19:28.521017Z","updated_at":"2026-10-14T16:55:38.306288Z","company_name":"Datadog","company_slug":"datadog","company_logo_url":"https://www.google.com/s2/favicons?domain=datadoghq.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/bed4ba83-1003-483b-ae16-2e373644bf3e"},{"id":"530711aa-611a-4a58-bb90-aff9ae5c0837","company_id":"a0000000-0000-0000-0000-000000000001","title":"Staff Research Engineer, Multi-Agent Scaling","slug":"staff-research-engineer-multi-agent-scaling-355bd081","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 Large teams of agents are starting to solve problems no single agent can, from rewriting major codebases to formalizing landmark mathematics. Our team studies how these teams scale: what happens to performance, cost and coordination as the number of agents, the compute budget and the length of the task grow, and what has to change to keep getting returns from that scale. We build the platform and evaluations Anthropic uses to run and measure large agent teams, and other research teams build on them.\n This role lives at the boundary between research and engineering. It is a generalist role on a small team: you'll design and run large experiments, build the systems they run on, and get to the bottom of surprising results. We often need to go from a vague question to a running experiment quickly.\n Responsibilities:\n \n Design, run and interpret large-scale experiments on agent teams, reasoning rigorously about what the data does and doesn't show\n Investigate how performance and efficiency change as team size, compute and task horizon grow, and find the bottlenecks that limit them\n Build and scale the systems that run very large agent teams reliably, and debug the failures that only appear at scale\n Design evaluations for long-horizon problems, and keep their results trustworthy\n Build the tooling and metrics that let researchers see what a large agent team is doing and why\n Partner with research teams across Anthropic so they can run their own experiments on the platform, and communicate findings clearly\n \n You may be a good fit if you:\n \n Have significant software engineering, ML or research engineering experience\n Have owned something substantial end to end, such as a large system, an evaluation or benchmark, an agent product, or a research project\n Genuinely enjoy both research and engineering work\n Think quantitatively about complex systems, and think twice before trusting a number\n Can work from a vague question rather than a spec\n Are results-oriented, with a bias towards flexibility and impact\n Have clear written and verbal communication\n Care about the societal impacts of your work\n \n Strong candidates may also have:\n \n Experience building or operating large-scale distributed systems, such as schedulers, sandboxed code execution, or inference and RL infrastructure\n Built evaluations, benchmarks or harnesses for LLMs or agents\n Experience building complex agentic systems that use LLMs\n Experience with scaling laws or other large-scale empirical research\n A background in operations research, statistics, economics, physics, quantitative finance, or another field that models and optimizes complex systems\n \n Strong candidates need not have:\n \n Formal certifications or education credentials\n Academic research experience or publication history\n Prior experience with multi-agent systems or reinforcement learning\n \n Representative projects:\n \n Measure how performance scales with the number of agents on a hard problem, and explain where the curve bends and why\n Prepare our largest-ever agent run: find what breaks as team size and task length grow together, and fix it before launch\n Work out how to allocate a fixed compute budget across a team of agents to solve a problem fastest\n Build tooling that turns the activity of a large agent team into something a researcher can read in minutes\n Design a novel eval that distinguishes real gains in teamwork from artifacts of the evaluation setup\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 ","salary_min":500000,"salary_max":850000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["reinforcement-learning","distributed-systems","search","agents","cloud","llm","alignment","research"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5442680008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-02T19:26:52Z","expires_at":"2026-11-13T16:54:56.618897Z","created_at":"2026-10-14T15:17:23.088259Z","updated_at":"2026-10-14T16:54:56.622222Z","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/530711aa-611a-4a58-bb90-aff9ae5c0837"},{"id":"65c8e5bf-cd0c-4f57-81c1-58c73a645c23","company_id":"2114efab-ea67-411b-bfb8-7899153105f3","title":"Member of Technical Staff, Forward Deployed Engineer","slug":"member-of-technical-staff-forward-deployed-engineer-8df75afb","description":"OVERVIEW\n\nInferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper 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\nWe're looking for a Forward Deployed Engineer to make Inferact successful inside real customer environments. You'll work directly with customers to deploy, integrate, debug, and optimize vLLM-powered inference systems across cloud, Kubernetes, GPU, networking, and model-serving environments.\n\nThis is a hands-on engineering role, not a traditional pre-sales position. You'll move from architecture discussions to implementation, own difficult production problems end-to-end, and work closely with core product and engineering teams to turn what you learn in the field into reusable product capabilities. Your work will directly affect customer time-to-value and how Inferact's platform evolves.\n\n\n\n\nSKILLS AND QUALIFICATIONS\n\nMinimum qualifications:\n\n - Bachelor's degree or equivalent experience in computer science, engineering, systems, machine learning, or similar.\n\n - Strong software engineering ability in Python, Go, TypeScript, or similar, with experience building production-quality integrations, tooling, services, automation, or prototypes.\n\n - Hands-on experience deploying or operating ML systems, model serving, AI infrastructure, cloud platforms, Kubernetes, or high-scale backend systems in production.\n\n - Ability to work directly with sophisticated customer engineering teams, understand ambiguous technical environments, and personally drive implementations and debugging to resolution.\n\n - Strong systems debugging skills across application, runtime, infrastructure, networking, identity, storage, observability, and distributed-system boundaries.\n\n - Ability to reason about latency, throughput, batching, model/runtime compatibility, scaling, reliability, and cost tradeoffs in production inference environments.\n\n - High ownership and strong technical communication, with the judgment to distinguish one-off customer work from problems that should become reusable product capabilities.\n\nPreferred qualifications:\n\n - Experience with vLLM, SGLang, TensorRT-LLM, TGI, Ray Serve, BentoML, or other LLM inference and model-serving systems.\n\n - Experience with NVIDIA or AMD GPUs, CUDA / ROCm, GPU scheduling, multi-GPU serving, or accelerator-backed infrastructure.\n\n - Experience deploying infrastructure software into enterprise, regulated, security-sensitive, or bring-your-own-cloud environments.\n\n - Experience building APIs, SDKs, CLIs, developer tooling, deployment platforms, control planes, or infrastructure products used by technical teams.\n\n - Experience profiling latency, throughput, concurrency, GPU utilization, bottlenecks, and performance regressions.\n\nBonus points if you have:\n\n - Contributed to open-source ML systems, inference infrastructure, cloud infrastructure, Kubernetes, or developer tooling.\n\n - Worked in a forward-deployed, customer engineering, field engineering, or highly technical solutions role where you personally wrote and shipped code.\n\n - Built deployment playbooks, reference architectures, automation, or tooling that materially reduced customer time-to-production.\n\n - Resolved severe customer-facing production issues that crossed multiple technical layers and required close partnership with core engineering.\n\n - Turned repeated customer problems into reusable product features, abstractions, documentation, or platform improvements.\n\n\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","mlops","cloud","research"],"apply_url":"https://jobs.ashbyhq.com/inferact/b127864f-5c71-4c96-90ce-cb20c57391a9/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-02T16:26:16.571Z","expires_at":"2026-11-13T16:58:50.520451Z","created_at":"2026-10-14T15:22:31.282956Z","updated_at":"2026-10-14T16:58:50.523264Z","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/65c8e5bf-cd0c-4f57-81c1-58c73a645c23"},{"id":"a5d6d300-9651-452a-a441-c56d808d2290","company_id":"a0000000-0000-0000-0000-000000000001","title":"Software Engineer, Sandboxing","slug":"software-engineer-sandboxing-1cf5c2c9","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 Every time Claude runs code, uses a computer, or works as an agent, it does so inside a sandbox. Our Sandboxing team builds the isolation layer behind that work. The same systems run the code Claude writes for users, the environments we train and evaluate models in, and the agents our customers deploy.\n These workloads are untrusted by design, and there are a lot of them. Sandboxes need to start in milliseconds, hold up under adversarial code, and scale to a very large number of concurrent sessions. You will work at the boundary between the Linux kernel, virtualization, and the container ecosystem. You will partner with research, product, and security teams to make sure that safe and fast do not trade off against each other.\n Key responsibilities\n \n \n Design, build, and operate the sandboxing infrastructure that runs untrusted, model-generated code and agent workloads at scale\n \n Improve sandbox startup time, density, and throughput, including snapshot and restore, checkpointing, and warm pools\n \n Strengthen isolation across compute, filesystem, and network, including egress controls and per-session credential handling\n \n Investigate and fix performance, reliability, and security issues at the kernel, hypervisor, and container runtime level\n \n Build the monitoring, tracing, and debugging tools that show where time and resources go inside a sandbox\n \n Work with researchers to support reinforcement learning environments and evaluations that need fast, reproducible, isolated execution\n \n Work with product and platform engineers to expose sandboxes as a dependable, well-designed capability for Claude products and customers\n \n Partner with security teams to threat-model the platform and to test, harden, and respond to escape and abuse scenarios\n \n Work with cloud providers on the hardware and platform features that our workloads depend on\n \n Share knowledge on systems programming and Linux internals, and help set technical standards for the team\n \n Minimum qualifications\n \n \n Software engineering experience in Linux systems programming, kernel development, or another low-level area\n \n Strong programming skills in at least one systems language such as Rust, C, C++, or Go\n \n Working knowledge of virtualization or container isolation technologies such as KVM, QEMU, Firecracker, gVisor, or runc\n \n Understanding of Linux resource management, including namespaces, cgroups, scheduling, and memory management\n \n Experience profiling and debugging performance issues at the system level\n \n Ability to work in unfamiliar codebases and technical areas, and to ship practical solutions with measurable results\n \n Strong communication skills and a commitment to building safe, reliable systems\n \n Preferred qualifications\n \n \n Experience building or operating a multi-tenant sandbox, serverless, or code execution platform\n \n Experience with microVMs, user-space kernels, or lightweight virtualization, including snapshot and restore\n \n Experience with OS-level security hardening, including seccomp, LSMs, and threat modeling of untrusted code\n \n Experience with eBPF and kernel tracing tools\n \n Experience with Kubernetes and cloud infrastructure at scale (AWS, GCP)\n \n Experience with network isolation, high-performance networking, or building egress proxies and policy layers\n \n Experience with sandboxes for AI agents, computer use, or reinforcement learning environments\n \n Experience with GPU virtualization, accelerators, or performance work on machine learning workloads\n \n Prior experience as a technical lead or mentor\n \n Representative projects\n \n \n Cutting sandbox cold start from seconds to milliseconds with snapshot-based restore and pre-warmed pools\n \n Designing a network egress policy layer so agents can reach approved services without exposing secrets\n \n Running many thousands of parallel reinforcement learning environments with strong isolation between them\n \n Building tracing tools that attribute latency and resource use inside a running sandbox\n \n Reducing per-sandbox memory overhead to raise density on each host\n \n Hardening the runtime against escape attempts found in internal red-team exercises\n The annual compensation range for this role is listed below. \n For sales roles, the range provided is the role’s On Target Earnings (\"OTE\") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\n Annual Salary:\n $320,000 — $485,000 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","salary_min":320000,"salary_max":485000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["reinforcement-learning","alignment","agents","cloud","research"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5440427008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-01T15:27:13Z","expires_at":"2026-11-13T16:54:56.554954Z","created_at":"2026-10-14T15:17:23.025457Z","updated_at":"2026-10-14T16:54:56.558118Z","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/a5d6d300-9651-452a-a441-c56d808d2290"},{"id":"300cb6b0-c010-49cc-9766-8c76be5ce1ea","company_id":"52f44519-9f93-4eac-ae0b-8be13e385ebe","title":"Research Engineer - Benchmarks","slug":"research-engineer-benchmarks-26845c22","description":"RESEARCH ENGINEER - BENCHMARKS\n\n\n\nEvery week, someone asks which data provider is actually best: for company records, for finding the right person, for fresh job listings. Right now the answers come from the vendors themselves. You'll build the independent version. You'll run rigorous, automated, public benchmarks of the providers in Alexandria and beyond, and publish them weekly. The same results will feed straight back into Alexandria so it learns which provider to call for which job.\n\nThis isn't our internal evals role. You're measuring the market, in public, where every number will be challenged by the vendors it ranks. You'll own it end to end: the datasets, the ground truth, the scoring, the harness, the weekly release, and the loop into Alexandria's provider selection. No one hands you a methodology. You write it, defend it, and ship it every week.\n\n\n\nSalary Range: $250,000–$290,000 USD/year (SF) / $210,000–$224,000 CAD/year (Toronto)\n\nEquity Range: Competitive equity. Details shared during the process.\n\nLocation: San Francisco, CA (SF HQ) or Toronto, ON (Toronto Hub). Hybrid, onsite 3+ days a week.\n\nEquity Range: Competitive equity. Details shared during the process.\n\nLocation: San Francisco, CA (SF HQ). On-site, five days a week.\n\nJob Type: Full-Time\n\nExperience: 4+ years in ML, research engineering, or data engineering, with evaluation or benchmark work you've shipped\n\nWork Authorization: Open to candidates already authorized to work in the US or Canada. In the US, we also support H-1B transfers, F-1 OPT and STEM OPT, TN (Canada and Mexico), and E-3 (Australia). We’re not able to sponsor new H-1B visas for candidates outside the US. For Canada, we’ll consider work permit sponsorship case by case through our Toronto Hub.\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. It's the boring-hard problem everyone building with LLMs eventually hits, solved.\n\nIn September 2026, we raised a $75M Series B led by Smash Capital, and we're spending it building the largest repository of knowledge in the world. We hit 8 figures in ARR in year one and more than doubled it in year two. We have 187k+ GitHub stars, putting us in the top 40 repositories of all time, 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. That library is called Alexandria, and it starts now.\n\n\n\n\nWHAT YOU'LL DO\n\n - Design and run head-to-head benchmarks of data providers against verified ground truth. For example:\n   \n   - Apollo vs. FullEnrich vs. DataLegion on company details.\n   \n   - FullEnrich vs. DataLegion on finding the right person and their current employer and title.\n   \n   - Built In vs. ZipRecruiter on relevant, fresh, non-duplicate job listings.\n\n - Build and maintain the test datasets and ground truth, and keep them from going stale or leaking.\n\n - Own the automated harness and the weekly benchmark release. Every run has to be reproducible, versioned, and defensible.\n\n - Measure what buyers actually care about: accuracy, coverage, freshness, speed, and cost.\n\n - Work with marketing to ship public leaderboard pages that are useful and hold up under scrutiny.\n\n - Close the loop into Alexandria so benchmark results change which provider gets called for what.\n\n - Keep expanding the categories we benchmark, inside Alexandria and beyond it.\n\n\n\n\nWHAT WE'RE LOOKING FOR\n\n - You've shipped evals or benchmarks, and you can explain exactly why your results were trustworthy.\n\n - You've done this somewhere that matters: a frontier lab, a data company like Scale, Surge, micro1 or Mercor, a third-party benchmark org, or a public benchmark project. OSS contributors very welcome.\n\n - You're strong in Python and API integrations, and comfortable with messy vendor APIs, rate limits, and inconsistent schemas.\n\n - You know how to build test sets and scoring methods: sampling, labeling, inter-rater agreement, and when to trust an LLM judge and when not to.\n\n - You use AI heavily and keep upgrading your own workflow. You ship without waiting for instructions.\n\n - You write findings clearly for engineers, marketers, and the vendors on the other side of the leaderboard.\n   \n   \n\n\nWHAT WE'RE NOT LOOKING FOR\n\n - Someone who wants to run benchmarks someone else designed.\n\n - Someone who picks the metric that makes the story look good. Our numbers have to survive the vendors","salary_min":210000,"salary_max":224000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["data-pipeline","search","agents","llm","payments","research"],"apply_url":"https://jobs.ashbyhq.com/firecrawl/90f88e13-7d5f-43e2-a3fc-9e16d836b71c/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-29T23:12:06.537Z","expires_at":"2026-11-13T17:00:14.068089Z","created_at":"2026-10-14T15:23:48.951555Z","updated_at":"2026-10-14T17:00:14.074182Z","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/300cb6b0-c010-49cc-9766-8c76be5ce1ea"},{"id":"5e7d165a-b53c-4ed3-9498-73273b6b1e83","company_id":"a0000000-0000-0000-0000-000000000001","title":"Research Engineer / Research Scientist, RL Frontiers","slug":"research-engineer-research-scientist-rl-frontiers-f4d3a293","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 Reinforcement learning is how Claude learns to reason, write code, and act autonomously over long horizons. The RL Scaling team works on how RL scales: what happens to throughput, stability, and learning efficiency as models get larger, episodes get longer, and compute grows by orders of magnitude, and what has to change in our algorithms and systems to keep getting returns from that scale.\n This role sits squarely across research and engineering. You'll develop next-generation architectures and RL algorithms, take them from a small-scale result to a frontier-scale run, and understand every place they behave differently along the way. You'll build the systems that set how fast the team can iterate: how many experiments, at what scale, and how quickly we can trust the results. And you'll work on Anthropic's largest and fastest RL runs, where the gap between a good idea and a working one is often a problem no one has solved yet.\n Key responsibilities\n \n Study how RL training and sampling scale with model size, context length, and compute, and find the algorithmic and systems changes that keep scaling efficient\n Develop next-generation model architectures and RL algorithms, and make them run efficiently at frontier scale\n Take promising small-scale results to frontier-scale runs, and diagnose why they behave differently when they get there, whether the cause is numerical, algorithmic, or systemic\n Build the experimental infrastructure that sets research velocity: fast, reproducible comparisons of architecture and algorithm variants at meaningful scale\n Own end-to-end performance of our largest RL runs, from research code down to the hardware\n Build performance and cost models for proposed architecture and algorithm changes, and use them to decide which ideas get scaled\n Investigate training dynamics at scale, including instabilities, divergence, and throughput regressions, and trace them to root cause\n \n Minimum qualifications\n \n Deep familiarity with modern transformer language models, including their architecture, training dynamics, and the behavior of large-scale optimization\n Hands-on experience training large models in a distributed setting, including the tradeoffs between data, tensor, and pipeline parallelism\n A track record of original technical work in ML training or systems, such as new methods, architectures, or optimizations, demonstrated through research, open-source, or production impact\n Ability to design rigorous experiments at scale, including baselines, ablations, and enough statistical care to trust a result that costs real compute\n Ability to reason quantitatively about the compute, memory, and communication costs of a model or algorithm\n Strong programming skills in Python and JAX or PyTorch, and comfort reading and changing code at every layer of the stack\n \n Preferred qualifications\n \n Research experience in reinforcement learning, optimization, or large-scale training, published or otherwise\n Experience developing RL algorithms for language models\n Experience with scaling laws or other quantitative models of training efficiency\n Experience designing or modifying transformer architectures beyond standard configurations\n Experience scaling training to large fleets of accelerators and debugging the problems that only appear at scale\n Deep understanding of numerics in large-scale training, including low-precision formats and sources of instability\n Familiarity with how GPU or TPU performance characteristics shape architecture and algorithm choices\n Experience with C++ or Rust\n \n Representative projects\n \n Characterize how a new RL algorithm's throughput and learning efficiency change from small models to frontier scale, and fix what breaks\n Develop a new attention variant, get it working at full scale, and measure how its quality and throughput compare to the baseline\n Prepare our next largest-ever RL run: find what breaks when model size, context length, and compute all grow at once, and fix it before launch\n Trace a loss instability that only appears past a certain scale to its root cause, and work out whether the fix belongs in the algorithm, the numerics, or the system\n Build a model that predicts the throughput and cost of a proposed architecture change before anyone writes the kernel\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’","salary_min":500000,"salary_max":850000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["alignment","jax","reinforcement-learning","cloud","pytorch","search","research"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5438044008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-29T19:03:37Z","expires_at":"2026-11-13T16:54:56.207892Z","created_at":"2026-10-14T15:17:22.661233Z","updated_at":"2026-10-14T16:54:56.210539Z","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/5e7d165a-b53c-4ed3-9498-73273b6b1e83"},{"id":"f52f56d2-71f3-41b7-97a4-f3faf0cfdfb8","company_id":"a0000000-0000-0000-0000-000000000001","title":"Research Engineer / Performance Engineer, RL Distributed Systems","slug":"research-engineer-performance-engineer-rl-distributed-systems-28be6fc4","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 Reinforcement learning is how Claude learns to reason, write code, and act autonomously over long horizons. At frontier scale, an RL run is an unusually demanding distributed system. Training, sampling, and environment execution run concurrently across a large fleet of accelerators and hosts, exchange data continuously, and have to keep making progress while hardware fails, load shifts, and the research changes underneath them. How well that system holds together determines how much of our compute turns into learning, and how quickly the team can try the next idea.\n As a Research Engineer on the Distributed Systems team within RL Engineering, you'll work on whatever part of that system is the current limit. That might be scheduling and placement, data movement between components, running large numbers of sandboxed environments, storage and checkpointing, networking, fault tolerance, autoscaling, or the observability that tells us what a run is actually doing. We're looking for generalists: engineers who can move between these layers, reason from first principles about a system they haven't seen before, and pick the problem that matters most rather than the one closest to their prior experience.\n Our system changes as fast as the research does, correctness under failure matters as much as throughput, and the best solutions often come from understanding the ML workload well enough to know which guarantees it actually needs. Strong candidates have built and run large distributed systems, care about getting the details right, and want to apply that experience to a workload that is very large, very heterogeneous, and changing quickly.\n Key responsibilities\n \n Design, build, and operate the distributed systems that run RL at scale, across training, sampling, and environment execution\n Find and remove whatever currently limits the system, whether it's scheduling, data movement, storage, networking, or coordination\n Build fault tolerance into every layer: failure detection, isolation, and recovery that keep long-running jobs making progress without human intervention\n Design resource management and autoscaling so that compute follows demand as a run's needs shift\n Build observability that makes it possible to understand what a run is doing and why it slowed down, stalled, or produced unexpected results\n Build automation that detects and remediates common problems, and design interfaces that let engineers and automated tools operate runs safely\n Work with researchers and performance engineers to make sure systems changes preserve training correctness and don't introduce subtle nondeterminism\n Remove classes of failure at their source through incident review, testing, and redesign, and write clear design documents for what you build\n \n Minimum qualifications\n \n Strong software engineering skills in Python and at least one systems language such as Rust, C++, or Go\n Experience designing, building, and operating large-scale distributed systems in production\n Deep understanding of distributed systems fundamentals, including consistency, coordination, consensus, failure modes, and recovery\n Ability to reason quantitatively about throughput, latency, and resource costs across compute, memory, storage, and network\n Experience debugging complex failures across many hosts and services, including failures you can't reproduce locally\n Strong written communication, including design documents and incident writeups\n \n Preferred qualifications\n \n Experience running ML training or inference infrastructure at scale\n Experience across several layers of the stack, such as scheduling, storage, networking, and orchestration\n Experience building schedulers, autoscalers, or resource management systems\n Experience with container orchestration such as Kubernetes, and with sandboxed or virtualized code execution at scale\n Experience with high-performance networking, RDMA, or collective communication libraries\n Experience building observability or automated remediation for large fleets\n Experience with async Python frameworks such as Trio or asyncio\n Familiarity with reinforcement learning or large language model training workloads\n \n Representative projects\n \n Design a scheduler that places training, sampling, and environment work across a heterogeneous cluster while respecting network topology and failure domains\n Build a failure detection and recovery system that lets a long-running job survive host and network failures with minimal lost work\n Scale environment execution substantially without increasing tail latency for the training step\n Design an autoscaling policy that rebalances compu","salary_min":500000,"salary_max":850000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["reinforcement-learning","llm","search","alignment","distributed-systems","research"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5438030008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-29T19:03:34Z","expires_at":"2026-11-13T16:54:56.168786Z","created_at":"2026-10-14T15:17:22.612611Z","updated_at":"2026-10-14T16:54:56.171453Z","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/f52f56d2-71f3-41b7-97a4-f3faf0cfdfb8"},{"id":"a0bd604b-bde2-4b7a-bd1a-1349371e22e1","company_id":"f8de0913-0ef7-4e72-a9cf-81f8513ec624","title":"Research / Software Engineer - Humanoid Whole Body Learning","slug":"research-software-engineer-humanoid-whole-body-learning-efc0c249","description":"FieldAI is transforming how robots interact with the real world. Our growing R\u0026D team is based in Boston, where we develop risk-aware, reliable, field-ready AI systems that tackle the hardest problems in robotics and unlock the potential of embodied intelligence. We take a pragmatic approach that goes beyond off-the-shelf, purely data-driven methods or transformer-only architectures, combining cutting-edge research with real-world deployment. Our solutions are already deployed globally, and we continuously improve model performance through rapid iteration driven by real field use.\n\n\nFieldAI is seeking a Software/Research Engineer to help build the learning systems that power our next generation of humanoid robots. You'll work across whole-body loco-manipulation, reinforcement learning, motion retargeting, and real-world robot deployment, turning new research and technical developments into reliable capabilities on physical humanoids. This is a highly hands-on role for someone excited about closing the loop between research, simulation, and robots operating in the real world.\n","salary_min":120000,"salary_max":220000,"location":"Boston, MA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"mid","tags":["search","reinforcement-learning","robotics","research"],"apply_url":"https://jobs.lever.co/field-ai/09def7d8-26bf-451f-a4f2-bd8fd4c5f5cf/apply","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-29T03:41:03.563Z","expires_at":"2026-11-13T17:00:37.943279Z","created_at":"2026-10-14T15:23:56.946161Z","updated_at":"2026-10-14T17:00:37.946561Z","company_name":"Field AI","company_slug":"field-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=field.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/a0bd604b-bde2-4b7a-bd1a-1349371e22e1"},{"id":"e0de87af-ad73-43f6-a502-ac3d19f5bc91","company_id":"4c0fefc3-173a-4227-a823-4d67d3e70ff0","title":"Research Engineer, Climate Modeling ","slug":"research-engineer-climate-modeling-5afb0300","description":"Persons in these roles are expected to work from our offices in Seattle. On-site requirements vary based on position and team. If you have questions about on-site work arrangements for this role, please ask your recruiter.\n Our base salary range is $146,880 - $220,320 and in addition we have generous bonus plans to provide a competitive compensation package.  \n We will begin reviewing applications on October 19, 2026. \n Who You Are: \n We seek a Research Engineer for a new three-year project to develop an AI-powered physics-informed hybrid climate model trained on historical data that is capable of multidecadal forecasts that are more accurate than the current state of the art. We’re looking for someone passionate about improving the quality, efficiency and accessibility of climate models with AI and modern software engineering.  \n This position is supported by a grant with a current period of performance ending September 22, 2029. Continuation of the position beyond that date depends on renewal, extension, or identification of additional funding. Ai2 will make reasonable efforts to notify the employee if funding for the position is expected to end. \n Who We Are:  \n Ai2’s Climate Modeling team is an international pioneer in using machine learning (ML) methodologies to improve on current physics-based climate models.  Our fast, accurate autoregressive open-source emulator, ACE, stably reproduces the climate and weather extremes of an existing climate model or a reanalysis. We have coupled ACE to a ML ocean model, trained it to  realistically account  for changing CO2 concentrations, and downscaled (super-resolved) ACE outputs from their 100 km native scale to to km-scale detail. Our tight-knit team of scientists and software engineers works closely with leading physics-based climate modeling centers to obtain unique training and testing data and get expert feedback on our progress. We collaborate with other leading research groups doing related AI work.\n Your Next Challenge: \n Physically-based climate models discretize equations representing individual time-evolving processes like clouds, rain, wind, land and sea-ice, and ocean currents on a computational grid.  Some processes (e.g. clouds) are less reliably encoded than others (e.g. winds).  Hence such climate models have biases in representing present-day climate and produce an undesirably large range of projections of future climate change for a given human-caused change in CO2 or other climate forcings.  Current AI-based climate models reduce present-day climate bias, but don’t generalize well to future climate change.\n We are starting a 3-year project funded by google.org  to develop an open source ‘AI-first’ climate model that can demonstrably project future climates more accurately than current physically-based climate models when trained on historical observations by judicious design of the AI to incorporate physical principles that reliably apply in any climate, seen or unseen.   The model should be lightweight and easy for an ML-savvy climate scientist to train and deploy. Our starting point is the Google Research NeuralGCM model, a hybrid ML model that uses a conventional discretization of winds encoded in JAX and learns a column-local representation of all other atmospheric and land processes from historical data. Compared to other AI climate models, NeuralGCM uses ML in a conceptually simpler way and shows somewhat better future-climate generalization skill.   It is an open-weight model but its training pipeline  and documentation need to be adapted for a broader user community.\n You will join a small team to develop the desired NeuralGCM+ model and foster its uptake by the climate modeling community.  This includes coupling it to an ML-based ocean model and designing approaches to demonstrably improve its skill in unseen climates.  This position has a three year term; there is potential for longer-term funding if  the NeuralGCM+ project demonstrates exceptional progress.\n You'll get to work on:\n \n Adapting the training pipeline and data needs of NeuralGCM to run efficiently on Google Cloud resources and on NVIDIA GPUs.  Note that NeuralGCM is written in JAX and implemented on TPUs.\n Documenting this training pipeline and distributing code on a GitHub repo.\n Implementing and testing a strategy for coupling NeuralGCM with the chosen ML ocean model.  \n Open-source code development as part of a tightly connected team, including daily meetings, code review, design documents and external collaborators. \n Develop and present tutorials on NeuralGCM+ to interested user groups.\n \n What You’ll Need: \n \n 2-4 years of experience building and deploying a relevant ML application in a practical or academic research setting and be fluent in Python and collaborative coding practices.\n A B.Sc. in computer or computational science, atmospheric science or a related geophysical science, or applied mathematics/statistics.\n Experience i","salary_min":146880,"salary_max":220320,"location":"Seattle, WA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["search","robotics","jax","research"],"apply_url":"https://job-boards.greenhouse.io/thealleninstitute/jobs/8234447","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-25T22:53:55Z","expires_at":"2026-11-13T17:02:42.380835Z","created_at":"2026-10-14T15:24:45.312892Z","updated_at":"2026-10-14T17:02:42.383908Z","company_name":"Allen Institute for AI","company_slug":"allen-institute-for-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=allenai.org\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e0de87af-ad73-43f6-a502-ac3d19f5bc91"},{"id":"0f46b7b7-9959-4d88-96d9-c6227e64bb30","company_id":"053355fc-0162-4bb9-b414-cbf7679ee9c8","title":"Research Scientist - Human-AI Systems","slug":"research-scientist-human-ai-systems-eefa08a8","description":"About Snorkel \n Snorkel AI is the frontier AI data lab, helping teams build the data and environments behind high-performing frontier and agentic AI. We combine technology with research-driven AI data development to create datasets, benchmarks, evals, and custom solutions for real-world AI systems. Founded out of the Stanford AI Lab in 2019, Snorkel works with leading AI labs and enterprises to move from better data to better outcomes.  Excited to help us redefine how AI is built? Apply to be the newest Snorkeler!\n \n \n We’re looking for a Research Scientist to advance how high-quality data and environments for AI agents are created. You’ll build and optimize pipelines that combine real-world data, automated generation, and human expert input. Working with domain experts, academic partners, customers, and our product and engineering teams, you’ll scale these pipelines to target frontier model performance gaps and expand data and environment diversity. Your work will amplify human knowledge and judgement, enabling experts to create and refine data and agentic environments that strengthens Snorkel’s position as the frontier data lab.\n This role is ideal for someone who wants to advance frontier AI through data and environment creation and enjoys turning research into reusable, scalable systems.\n Location: San Francisco, New York City,  OR REMOTE \n Main Responsibilities \n \n Design, implement, and optimize reusable pipelines that combine AI capabilities with expert judgment to accelerate data and agentic environment creation.\n Design and run rigorous experiments to validate proof-of-concept approaches, measure their impact on data quality, pipeline efficiency, and model performance, and communicate findings.\n Partner with engineering, data operation, domain experts, and customers to turn research prototypes into reliable production workflows.\n Work directly with domain experts to design and test workflows that help them author, review, and refine data and environments.\n Work cross-functionally with data operations, product, and engineering to surface research findings that inform the company roadmap. \n Stay at the frontier of research in data and agentic environment creation and bring best practices into Snorkel's workflows.\n Represent Snorkel's research externally through publications, blog posts, conference talks, and customer engagements that advance the conversation around data-centric AI.\n \n Preferred Qualifications \n \n Strong research background in AI, machine learning, NLP, LLMs, or related fields, with experience developing and evaluating new methods.\n Experience building environments for AI agents in automated research, computer use, coding, or professional domain workflows.\n Experience with one or more of synthetic data generation, human in the loop workflows, reinforcement learning, agent environments, or model evaluation.\n Strong experimental design skills, including defining hypotheses and conducting ablations.\n Experience with software engineering best practices (e.g., clean coding, modular design, version control).\n Ability to collaborate with domain experts and translate their knowledge into concrete tasks, evaluation criteria, and repeatable workflows.\n Comfort with rapid iteration, ambiguous research questions, and moving ideas from experimentation into production.\n Ph.D. in machine learning, NLP, or a related field preferred; equivalent industry or research lab experience considered.\n \n \n  \n Actual compensation will be determined based on factors including skills, qualifications, experience, and geographic location.\n Salary range(s) for this role\n $200,000 — $375,000 USD \n Be Your Best at Snorkel \n Joining Snorkel AI means becoming part of a company that has market proven solutions, robust funding, and is scaling rapidly—offering a unique combination of stability and the excitement of high growth. As a member of our team, you’ll have meaningful opportunities to shape priorities and initiatives, influence key strategic decisions, and directly impact our ongoing success. Whether you’re looking to deepen your technical expertise, explore leadership opportunities, or learn new skills across multiple functions, you’re fully supported in building your career in an environment designed for growth, learning, and shared success.\n Snorkel AI is proud to be an Equal Employment Opportunity employer and is committed to building a team that represents a variety of backgrounds, perspectives, and skills. Snorkel AI embraces diversity and provides equal employment opportunities to all employees and applicants for employment. Snorkel AI prohibits discrimination and harassment of any type on the basis of race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local law. All employment is decided on the basis of qualifications, perfor","salary_min":200000,"salary_max":375000,"location":"New York, NY","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"senior","tags":["reinforcement-learning","llm","nlp","agents","research"],"apply_url":"https://job-boards.greenhouse.io/snorkelai/jobs/6208895004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-25T20:17:42Z","expires_at":"2026-11-13T16:55:33.51421Z","created_at":"2026-10-14T15:19:23.633811Z","updated_at":"2026-10-14T16:55:33.516936Z","company_name":"Snorkel AI","company_slug":"snorkel-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=snorkel.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/0f46b7b7-9959-4d88-96d9-c6227e64bb30"},{"id":"62db2168-bd15-49b2-b85d-c16fb830a1b2","company_id":"4bc4e268-7a05-4a65-a162-1688af546f7e","title":"Principal Research Engineer, Unreal Assistant","slug":"principal-research-engineer-unreal-assistant-b6414563","description":"WHAT MAKES US EPIC?\n At the core of Epic’s success are talented, passionate people. Epic prides itself on creating a collaborative, welcoming, and creative environment. Whether it’s building award-winning games or crafting engine technology that enables others to make visually stunning interactive experiences, we’re always innovating.\n Being Epic means being a part of a team that continually strives to do right by our community and users. We’re constantly innovating to raise the bar of engine and game development.\n PROGRAMMING - UNREAL ENGINE\n What We Do \n Unreal-powered projects have been on the bleeding edge of real-time entertainment for over 20 years. Our team of programming experts are always innovating to improve the tools and technology that empower content developers worldwide.\n What You'll Do \n We're looking for a Principal Al Programmer who builds products that integrate Al into Unreal Engine. You'll design and ship the full stack of systems that let LLMs act inside the engine (from end to end, not just a single layer), bringing depth in game engine architecture, cloud services, and how LLMs actually work.\n In this role, you will \n \n Design and build LLM-driven agents and tools that operate directly inside Unreal Engine, including engine-side C++/Python tooling and editor scripting\n Design and extend the cloud-hosted agent harness, services, and database that mediate between LLMs and the engine\n Work across the full stack (engine tooling, cloud backend, and the web UI used to drive and monitor agents) to ship end-to-end features\n Evaluate and integrate emerging LLM capabilities (tool use, multi-agent orchestration, context management) into Epic's engine and pipeline tooling\n Define technical direction for the reliability, safety, and evaluation of LLM-driven engine automation\n Partner closely with engine, tools, and backend programmers, and mentor others on both game engine internals and LLM system design\n \n What we're looking for \n \n Strong software programming background with hands-on experience in a game engine (Unreal Engine strongly preferred), including engine architecture and tooling/editor scripting\n Practical experience building or shipping products powered by LLMs (agents, tool-calling, RAG, prompt/context programming), or a strong conceptual understanding of how LLMs work\n Experience with cloud-native services and infrastructure: web APIs, databases, and service architecture and deployment\n Excellent programming skills across C++ (engine-side) and Python and/or a web backend language\n Ability to work full-stack and reason about tradeoffs across engine code, backend services, and a web front end\n Excellent written and verbal communication skills\n BS/MS in Computer Science or a related field\n \n This role is open to multiple locations in North America (including CA, NYC, \u0026 WA). \n Pay Transparency Information \n The expected annual base pay range(s) for this position are detailed below. Each base pay range is relevant only for individuals who are residents of or will be expected to work within the specified locale. Compensation varies based on a variety of factors, which include (but aren’t limited to) things such as skills and competencies, qualifications, knowledge, and experience. In addition to base pay, most employees are eligible to participate in Epic’s generous benefit plans and discretionary incentive programs (subject to the terms of those plans or programs). \n New York City Base Pay Range\n $245,975 — $360,763 USD \n California Base Pay Range\n $270,572 — $396,838 USD \n Washington Base Pay Range\n $245,975 — $360,763 USD \n ABOUT US\n Epic Games​ ​is a leading interactive entertainment company. For over 30 years we've been making award-winning games and engine technology that empowers others to make visually stunning games and 3D content that bring environments to life like never before. Epic's award-winning Unreal Engine technology not only provides game developers the ability to build high-fidelity, interactive experiences for PC, console, mobile, and VR, it is also a tool being embraced by content creators across a variety of industries such as media and entertainment, automotive, and architectural design. As we continue to build our Engine technology and develop remarkable games, we strive to build teams of world-class talent.\n Like what you hear? Come be a part of something Epic! \n Epic Games deeply values diverse teams and an inclusive work culture, and we are proud to be an Equal Opportunity employer. Learn more about our Equal Employment Opportunity (EEO) Policy here .\n Note to Recruitment Agencies: Epic does not accept any unsolicited resumes or approaches from any unauthorized third party (including recruitment or placement agencies) (i.e., a third party with whom we do not have a negotiated and validly executed agreement). We will not pay any fees to any unauthorized third party. Further details on these matters can be found here .","salary_min":245975,"salary_max":360763,"location":"BLANK,BLANK,Multiple Locations","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["llm","agents","search","research"],"apply_url":"https://epicgames.com/careers/jobs/6200782004?gh_jid=6200782004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-25T13:51:18Z","expires_at":"2026-11-13T17:02:44.861895Z","created_at":"2026-10-14T15:24:47.951049Z","updated_at":"2026-10-14T17:02:44.864932Z","company_name":"Epic Games","company_slug":"epic-games","company_logo_url":"https://www.google.com/s2/favicons?domain=epicgames.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/62db2168-bd15-49b2-b85d-c16fb830a1b2"},{"id":"ce19185e-7970-4df0-8c9e-c3dac4229c7c","company_id":"a0000000-0000-0000-0000-000000000007","title":"MTS, Agent Research","slug":"mts-agent-research-b61a23a7","description":"About Etched\n\nEtched is building hardware for frontier intelligence. We co-design chips, racks, software, and manufacturing to deliver best-in-class throughput and latency across both prefill and decode workloads. Our first products are heavily focused on inference. Backed by hundreds of millions from top-tier investors and staffed by leading engineers, Etched is redefining the infrastructure layer for the fastest growing industry in history.\n\n\n\nJob Summary\n\nAs a research lead, you will set the research direction for this effort and spearhead the development of self-improving agents that reliably complete difficult, long-running engineering tasks. At Etched, this loop compounds: better agents help us build and optimize our systems faster, better systems expand our capacity to run more experiments, generating feedback and training data that further improve agent capabilities. Your job is to make this loop work in practice.\n\nThis requires strong research judgment. Where will better memory, context, or tooling unlock new capabilities? Which tasks benefit from structured workflows, and which require more open-ended autonomy? When do agent limitations call for changes to the harness, and when do they require custom models trained with supervised fine-tuning or reinforcement learning? You will set research priorities and design experiments that guide our investments in in-context learning, model training, and harness design.\n\nYou will work closely with engineers embedded across the company, with an initial focus on ASIC development, inference, and supercomputing. First, you will build the environments that let existing models do their best work through better tools, context, feedback, and in-context learning. Then, you will use the resulting trajectories and evaluations to develop specialized models where they can improve capability, reliability, or cost.\n\nThis is a hands-on research leadership role. You should be comfortable setting direction, writing and debugging code, and carrying an idea through to production. Research success means measurable improvements in the work Etched can accomplish, with disciplined use of compute.\n\n\n\nKey Responsibilities\n\n - Own the research agenda for agentic systems across Etched. Identify the most important bottlenecks, prioritize promising approaches, and decide when to scale.\n\n - Work directly with domain engineers to understand real workflows, diagnose where agents fail, and turn those failures into research questions with concrete evaluations.\n\n - Develop systems for long-running tasks, including memory, context management, tool use, planning, and coordination between agents. Determine which approaches generalize across teams and which require domain-specific methods.\n\n - Build evaluations that measure correctness, reliability, and efficiency on real engineering tasks, and verify that gains transfer to production.\n\n - Train custom models using SFT, RL, and distillation, with the agent harness in the training loop. Use execution trajectories and failure analysis to guide training data and reward design.\n\n - Scale experiments to hundreds or thousands of concurrent agents while maintaining reproducibility, observability, and control over compute costs.\n\n\n\nYou may be a good fit if you have (Must-have qualifications)\n\n - Strong research taste and a record of turning ambiguous problems into clear hypotheses, decisive experiments, and useful systems.\n\n - Excellent engineering ability. You can move between research exploration, agent experimentation, low-level debugging, and production execution, and you take responsibility for systems working reliably.\n\n - Experience building agents for complex, multi-step tasks, with a practical understanding of context, memory, tools, evaluation, and failure recovery.\n\n - Hands-on experience with LLM post-training, including SFT and RL.\n\n - A track record of making meaningful progress under compute and data constraints. You care about the quality of the experiment and the cost of a successful outcome.\n\n - High agency and comfort with unfamiliar domains. You can find the problem, acquire the missing context, and drive the work forward.\n\n - The ability to set technical direction and work closely with domain experts to turn research into systems they trust and use.\n\n\n\nStrong candidates may also have experience with (Nice-to-have qualifications)\n\n - ASIC design or verification, accelerator kernels, inference systems, or large-scale compute infrastructure.\n\n\n\nBenefits\n\n - Medical, dental, and vision packages with generous premium coverage\n   \n   - $500 per month credit for waiving medical benefits\n\n - Housing subsidy of $2,500 per month for those living within walking distance of the office\n\n - Relocation support for those moving to San Jose (Santana Row)\n\n - Various wellness benefits covering fitness, mental health, and more\n\n - Daily lunch and dinner in our office\n\n - Unlimited compute budget subject to ROI justification\n\n\n\nBase Compensation Range\n\n ","salary_min":175000,"salary_max":275000,"location":"San Jose, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["fine-tuning","llm","agents","reinforcement-learning","research"],"apply_url":"https://jobs.ashbyhq.com/etched/37340964-10d7-4cfc-995d-f41f3819ccc2/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-25T03:29:00.106Z","expires_at":"2026-11-13T16:56:00.049257Z","created_at":"2026-10-14T15:20:09.563097Z","updated_at":"2026-10-14T16:56:00.05352Z","company_name":"Etched","company_slug":"etched","company_logo_url":"https://www.google.com/s2/favicons?domain=etched.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/ce19185e-7970-4df0-8c9e-c3dac4229c7c"},{"id":"7fc13b63-04a8-44e0-a61f-15ae3df48f22","company_id":"46e2df02-755e-46ce-a584-3f5881f34183","title":"Staff Research Scientist","slug":"staff-research-scientist-c2c0a1ea","description":"About Turing \n Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at www.turing.com .  \n  \n The Role\n Turing is seeking exceptional  Research Scientists to join our research organization and develop new ways to evaluate, train, and improve frontier AI systems.\n This is a research-first role focused on problems where the right benchmark, dataset, or methodology often does not yet exist. You will identify important gaps in the literature, propose ambitious new research directions, and take projects from initial hypothesis through experimentation, benchmark construction, and publication.\n Our research is deliberately focused on frontier evaluation, synthetic data, hallucination and reliability, and agentic science . We are looking for scientists who can recognize important problems early, formulate them precisely, and design rigorous research programs to answer them.\n What You'll Do \n Frontier benchmarks and evaluation \n \n Identify high-impact gaps in existing benchmark and evaluation literature.\n Design novel benchmarks in and across STEM fields and on general model functionality.\n Develop evaluations for emerging model capabilities that are poorly captured by traditional static benchmarks.\n Design rigorous task-generation, grading, contamination-control, difficulty-calibration, and validation methodologies.\n Build benchmarks that can become both valuable research contributions and meaningful standards for evaluating frontier models.\n \n Synthetic data and post-training \n \n Develop methods for generating high-quality synthetic STEM training data.\n Study how task selection, difficulty, diversity, verification, filtering, and data quality affect downstream performance.\n Explore methods for generating useful training signal in domains where expert human data is scarce or expensive.\n Design experiments that determine when synthetic data genuinely improves capabilities rather than simply increasing training volume.\n \n Hallucination, reliability, and verification \n \n Study hallucination, uncertainty, calibration, and epistemic failure in technical domains.\n Develop evaluations and methods for improving factual reliability, self-correction, verification, citation, and appropriate abstention.\n Investigate when models should reason internally, invoke tools, seek external evidence, or recognize that they do not know.\n \n Agentic science \n \n Research AI systems capable of performing extended scientific and technical work.\n Develop workflows involving literature search, coding, simulation, tool use, experimentation, verification, and iterative reasoning.\n Evaluate long-horizon scientific agents and identify the bottlenecks preventing them from reliably performing real research.\n Explore new approaches to human-AI and multi-agent scientific collaboration.\n \n New research directions \n The areas above are our core focus, not an exhaustive list. Researchers will also have significant latitude to propose new programs in areas such as reasoning, model evaluation, AI-for-science, data generation, and emerging capabilities.\n What We’re Looking For\n \n PhD or equivalent research experience in machine learning, computer science, mathematics, physics, chemistry, biology, engineering, statistics, or another highly technical field .\n Demonstrated ability to formulate and execute original research.\n Strong understanding of modern LLMs and the frontier AI research landscape.\n Excellent experimental design, quantitative reasoning, and scientific judgment.\n Ability to rapidly understand unfamiliar technical literature and develop expertise in new areas.\n Strong Python skills and the ability to independently build research prototypes and evaluation pipelines.\n Excellent technical writing and communication.\n Comfort working in a fast-moving environment where the research agenda evolves with the frontier.\n \n A strong publication record is valuable, but we care most about whether you can identify important questions, design rigorous ways to answer them, and execute quickly enough for the results to matter .\n A note from our CTO, Ece Kamar \n Some of the most important questions in AI can't be answered from inside ","salary_min":250000,"salary_max":400000,"location":"United States","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["reinforcement-learning","agents","healthcare","llm","research"],"apply_url":"https://job-boards.greenhouse.io/turing/jobs/6187280004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-24T16:27:52Z","expires_at":"2026-11-13T16:55:56.016491Z","created_at":"2026-10-14T15:20:05.469857Z","updated_at":"2026-10-14T16:55:56.021479Z","company_name":"Turing","company_slug":"turing","company_logo_url":"https://www.google.com/s2/favicons?domain=turing.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/7fc13b63-04a8-44e0-a61f-15ae3df48f22"},{"id":"78cec453-f9c8-4f1a-880c-377d3eb9eb48","company_id":"46e2df02-755e-46ce-a584-3f5881f34183","title":"Senior Research Scientist, STEM","slug":"senior-research-scientist-stem-4bc94d78","description":"About Turing \n Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at www.turing.com .  \n  \n The Role\n Turing is seeking exceptional  Research Scientists to join our STEM research organization and develop new ways to evaluate, train, and improve frontier AI systems.\n This is a research-first role focused on problems where the right benchmark, dataset, or methodology often does not yet exist. You will identify important gaps in the literature, propose ambitious new research directions, and take projects from initial hypothesis through experimentation, benchmark construction, and publication.\n Our research is deliberately focused on frontier STEM evaluation, synthetic data, hallucination and reliability, and agentic science . We are looking for scientists who can recognize important problems early, formulate them precisely, and design rigorous research programs to answer them.\n What You'll Do \n Frontier benchmarks and evaluation \n \n Identify high-impact gaps in existing benchmark and evaluation literature.\n Design novel benchmarks in and across STEM fields and on general model functionality.\n Develop evaluations for emerging model capabilities that are poorly captured by traditional static benchmarks.\n Design rigorous task-generation, grading, contamination-control, difficulty-calibration, and validation methodologies.\n Build benchmarks that can become both valuable research contributions and meaningful standards for evaluating frontier models.\n \n Synthetic data and post-training \n \n Develop methods for generating high-quality synthetic STEM training data.\n Study how task selection, difficulty, diversity, verification, filtering, and data quality affect downstream performance.\n Explore methods for generating useful training signal in domains where expert human data is scarce or expensive.\n Design experiments that determine when synthetic data genuinely improves capabilities rather than simply increasing training volume.\n \n Hallucination, reliability, and verification \n \n Study hallucination, uncertainty, calibration, and epistemic failure in technical domains.\n Develop evaluations and methods for improving factual reliability, self-correction, verification, citation, and appropriate abstention.\n Investigate when models should reason internally, invoke tools, seek external evidence, or recognize that they do not know.\n \n Agentic science \n \n Research AI systems capable of performing extended scientific and technical work.\n Develop workflows involving literature search, coding, simulation, tool use, experimentation, verification, and iterative reasoning.\n Evaluate long-horizon scientific agents and identify the bottlenecks preventing them from reliably performing real research.\n Explore new approaches to human-AI and multi-agent scientific collaboration.\n \n New research directions \n The areas above are our core focus, not an exhaustive list. Researchers will also have significant latitude to propose new programs in areas such as reasoning, model evaluation, AI-for-science, data generation, and emerging capabilities.\n What We’re Looking For\n \n PhD or equivalent research experience in machine learning, computer science, mathematics, physics, chemistry, biology, engineering, statistics, or another highly technical field .\n Demonstrated ability to formulate and execute original research.\n Strong understanding of modern LLMs and the frontier AI research landscape.\n Excellent experimental design, quantitative reasoning, and scientific judgment.\n Ability to rapidly understand unfamiliar technical literature and develop expertise in new areas.\n Strong Python skills and the ability to independently build research prototypes and evaluation pipelines.\n Excellent technical writing and communication.\n Comfort working in a fast-moving environment where the research agenda evolves with the frontier.\n \n A strong publication record is valuable, but we care most about whether you can identify important questions, design rigorous ways to answer them, and execute quickly enough for the results to matter .\n A note from our CTO, Ece Kamar \n Some of the most important questions in AI can't be answered fr","salary_min":250000,"salary_max":350000,"location":"United States","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["llm","reinforcement-learning","healthcare","agents","research"],"apply_url":"https://job-boards.greenhouse.io/turing/jobs/6187281004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-24T16:26:48Z","expires_at":"2026-11-13T16:55:55.999304Z","created_at":"2026-10-14T15:20:05.454175Z","updated_at":"2026-10-14T16:55:56.003165Z","company_name":"Turing","company_slug":"turing","company_logo_url":"https://www.google.com/s2/favicons?domain=turing.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/78cec453-f9c8-4f1a-880c-377d3eb9eb48"},{"id":"14b6b5c9-21b3-4f18-a3e5-0e02f93abb2c","company_id":"46e2df02-755e-46ce-a584-3f5881f34183","title":"Research Scientist, STEM","slug":"research-scientist-stem-831b3be0","description":"About Turing \n Turing’s mission is to accelerate superintelligence to drive real economic progress. 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Researchers will also have significant latitude to propose new programs in areas such as reasoning, model evaluation, AI-for-science, data generation, and emerging capabilities.\n What We’re Looking For\n \n PhD or equivalent research experience in machine learning, computer science, mathematics, physics, chemistry, biology, engineering, statistics, or another highly technical field .\n Demonstrated ability to formulate and execute original research.\n Strong understanding of modern LLMs and the frontier AI research landscape.\n Excellent experimental design, quantitative reasoning, and scientific judgment.\n Ability to rapidly understand unfamiliar technical literature and develop expertise in new areas.\n Strong Python skills and the ability to independently build research prototypes and evaluation pipelines.\n Excellent technical writing and communication.\n Comfort working in a fast-moving environment where the research agenda evolves with the frontier.\n \n A strong publication record is valuable, but we care most about whether you can identify important questions, design rigorous ways to answer them, and execute quickly enough for the results to matter .\n A note from our CTO, Ece Kamar \n Some of the most important questions in AI can't be answered f","salary_min":150000,"salary_max":300000,"location":"United States","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["agents","llm","healthcare","reinforcement-learning","research"],"apply_url":"https://job-boards.greenhouse.io/turing/jobs/6187283004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-24T16:26:19Z","expires_at":"2026-11-13T16:55:55.974648Z","created_at":"2026-10-14T15:20:05.434266Z","updated_at":"2026-10-14T16:55:55.977792Z","company_name":"Turing","company_slug":"turing","company_logo_url":"https://www.google.com/s2/favicons?domain=turing.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/14b6b5c9-21b3-4f18-a3e5-0e02f93abb2c"}],"page":1,"per_page":20,"total":757,"total_is_exact":true,"total_pages":38}