Anthropic· AI Research & Engineering· San Francisco, CA
Research Engineer, Performance RL
Classified Tasks (18)
Automate 0%Augment 67%Human-Only 33%
Augment (12)
AI assists, human decides
Develop systems that enable models to use computers effectively
technical
Advance code generation through reinforcement learning
technical
Build scalable RL infrastructure and training methodologies
technical
Enhance model reasoning capabilities
technical
Partner with the applied production training team to bring research innovations into deployed models
operational
Implement research at scale
operational
Advance models' ability to safely write correct, fast code for accelerators
technical
Translate accelerator performance characteristics into tasks and learning signals for models
analytical
Design RL environments and evaluations
technical
Implement RL environments and evaluations
technical
Conduct experiments
analytical
Deliver work into training runs
operational
Human-Only (6)
Requires human judgment
Lead reinforcement learning research and development
leadership
Pioneer fundamental RL research for large language models
creative
Collaborate closely with alignment and frontier red teams to ensure systems are capable and safe
communication
Invent RL environments and evaluations
creative
Shape the research roadmap
leadership
Collaborate with researchers, engineers, and performance engineering specialists across and outside Anthropic
communication
Job description
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 RL 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 4.6 and Opus 4.6. 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 We're hiring for the Code RL team within the RL organization. As a Research Engineer, you'll advance our models' ability to safely write correct, fast code for accelerators. You'll need to know accelerator performance well to turn it into tasks and signals models can learn from. Specifically, you will: Invent, design and implement RL environments and evaluations. Conduct experiments and shape our research roadmap. Deliver your work into training runs. Collaborate with other researchers, engineers, and performance engineering specialists across and outside Anthropic. You may be a good fit if you: Have expertise with accelerators (CUDA, ROCm, Triton, Pallas), ML framework programming (JAX or PyTorch). Have worked across the stack – kernels, model code, distributed systems. Know how to balance research exploration with engineering implementation. Are passionate about AI's potential and committed to developing safe and beneficial systems. Strong candidates may also have: Experience with reinforcement learning. Experience porting ML workloads between different types of accelerators. Familiarity with LLM training methodologies. 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: $350,000 — $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 co