# Research Engineer, Machine Learning (Reinforcement Learning) Source: https://realanalystjobs.com/job/research-engineer-machine-learning-reinforcement-learning-anthropic-d55ec4de Research Engineer, Machine Learning (Reinforcement Learning) at Anthropic, an on-site role based in San Francisco, New York, USA. Pay stated in the posting: $500,000 to $850,000. Posted on 1 April 2025, read from Anthropic's own careers page (Greenhouse), which RealAnalystJobs re-checks daily and removes the role when it closes. The posting says the employer sponsors visas or will consider it. Anthropic has approved H-1B petitions on USCIS record (employer-wide, not a promise for this role). Updated 15 November 2026. ## Key facts - Company: [Anthropic](https://realanalystjobs.com/companies/anthropic) - Location: San Francisco, New York, USA - Work mode: On-site - Pay stated in the posting: $500,000 to $850,000 - Posted: Apr 1, 2025 - Employment type: Full time - Source: Anthropic's careers page (Greenhouse), re-checked daily Skills: [Python](https://realanalystjobs.com/skills/python) ## Visa sponsorship The posting says the employer sponsors visas or will consider it. Confirm with the employer before you apply. Anthropic had H-1B petitions approved for 189 workers in fiscal year 2026 (USCIS). That is history, not a promise for this role. [See the record](https://realanalystjobs.com/companies/anthropic#h1b-record) ## About this role About Anthropic 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. About the teams Our Reinforcement Learning teams lead Anthropic's reinforcement learning research and development, playing a critical role in advancing our AI systems. We've contributed to all Claude models, with significant impacts on the autonomy and coding capabilities of Claude Sonnet 4.5 and Opus 4.5. Our work spans several key areas: • Developing systems that enable models to use computers effectively • Advancing code generation through reinforcement learning • Pioneering fundamental RL research for large language models • Building scalable RL infrastructure and training methodologies • Enhancing model reasoning capabilities We collaborate closely with Anthropic's alignment and frontier red teams to ensure our systems are both capable and safe. We partner with the applied production training team to bring research innovations into deployed models, and are dedicated to implement our research at scale. Our Reinforcement Learning teams sit at the intersection of cutting-edge research and engineering excellence, with a deep commitment to building high-quality, scalable systems that push the boundaries of what AI can accomplish. About the Role As a Research Engineer within Reinforcement Learning, you will collaborate with a diverse group of researchers and engineers to advance the capabilities and safety of large language models. This role blends research and engineering responsibilities, requiring you to both implement novel approaches and contribute to the research direction. You'll work on fundamental research in reinforcement learning, creating 'agentic' models via tool use for open-ended tasks such as computer use and autonomous software generation, improving reasoning abilities in areas such as mathematics, and developing prototypes for internal use, productivity, and evaluation. Representative projects: • Architect and optimize core reinforcement learning infrastructure, from clean training abstractions to distributed experiment management across GPU clusters. Help scale our systems to handle increasingly complex research workflows. • Design, implement, and test novel training environments, evaluations, and methodologies for reinforcement learning agents which push the state of the art for the next generation of models. • Drive performance improvements across our stack through profiling, optimization, and benchmarking. Implement efficient caching solutions and debug distributed systems to accelerate both training and evaluation workflows. • Collaborate across research and engineering teams to develop automated testing frameworks, design clean APIs, and build scalable infrastructure that accelerates AI research. You may be a good fit if you: • Are proficient in Python and async/concurrent programming with frameworks like Trio • Have experience with machine learning frameworks (PyTorch, TensorFlow, JAX) • Have industry experience in machine learning research • Can balance research exploration with engineering implementation • Enjoy pair programming (we love to pair!) • Care about code quality, testing, and performance • Have strong systems design and communication skills • Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems Strong candidates may have: • Familiarity with LLM architectures and training methodologies • Experience with reinforcement learning techniques and environments • Experience with virtualization and sandboxed code execution environments • Experience with Kubernetes • Experience with distributed systems or high-performance computing • Experience with Rust and/or C++ Strong candidates need not have: • Formal certifications or education credentials • Academic research experience or publication history Deadline to apply: None. Applications will be reviewed on a rolling basis. The annual compensation range for this role is listed below. 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. Annual Salary: $500,000 - $850,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position 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. 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. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. 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 Description from Anthropic's public careers feed, reproduced so you can read the role here. Apply on the company's own site; Real Analyst Jobs never submits anything for you. ## How to apply Open [this role on Real Analyst Jobs](https://realanalystjobs.com/job/research-engineer-machine-learning-reinforcement-learning-anthropic-d55ec4de) and use Apply now there to reach the employer's application. The listing is re-checked against Anthropic's careers page daily and removed when it closes. ## Anthropic 11 open analyst roles on Real Analyst Jobs. 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