Research Engineer, RL Engineering

<div class="content-intro"><h2><strong>About Anthropic</strong></h2> <p>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.</p></div><h2>About the role</h2> <p>Reinforcement learning (RL) is how Claude learns to reason, write code, and act autonomously over long horizons. This role sits on the team that builds and owns the RL training system: the system that trains our production models and that researchers across Anthropic run their experiments on. The team works closely with research teams across the company on the science and engineering of making RL work at scale.</p> <p>As a Research Engineer on the team, you'll work at the center of RL at Anthropic. You'll have a direct view of how RL training behaves at the frontier because the system you own sits underneath both production and research runs. You will use it with collaborators across research teams to understand what is working, what is fragile, and where the next improvements are.</p> <h2>Key responsibilities</h2> <ul> <li>Build, own, and improve the core RL training system that serves Anthropic's production and research runs</li> <li>Work across the stack (orchestration, environments, training, inference, evaluation) wherever the system needs it</li> <li>Study how RL training behaves at scale and contribute to the research that improves it, in collaboration with teams across Anthropic</li> <li>Implement new training methods as stable, fast, well-tested code</li> <li>Improve the speed and efficiency of RL training and evaluation through profiling, optimization, and benchmarking</li> <li>Make the system easier for researchers to build on, through clean abstractions, clear APIs, and automated testing</li> <li>Debug hard problems across the stack