Research Engineer, RL Engineering

Job summary

Responsibilities

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.

• Build, own, and improve the core RL training system that serves Anthropic's production and research runs
• Work across the stack (orchestration, environments, training, inference, evaluation) wherever the system needs it
• Study how RL training behaves at scale and contribute to the research that improves it, in collaboration with teams across Anthropic
• Implement new training methods as stable, fast, well-tested code
• Improve the speed and efficiency of RL training and evaluation through profiling, optimization, and benchmarking

What they're looking for

• Proficiency in Python and experience working in, debugging, and improving a large ML codebase
• Experience with large-scale machine learning training (reinforcement learning, pretraining, or post-training) or the systems that support it
• Experience with at least one modern ML framework (JAX, PyTorch, or similar)
• Ability to design controlled experiments and reach conclusions you and others can trust
• Ability to balance research exploration with engineering implementation

Tech stack

• Python
• JAX
• PyTorch
• Rust
• C++