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.
