About the company
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.
About the role
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.
Responsibilities
- 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
- 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
- Make the system easier for researchers to build on, through clean abstractions, clear APIs, and automated testing
- Debug hard problems across the stack, from a run that has quietly drifted to a distributed systems failure that only shows up at scale
- Communicate results clearly, in writing and in discussion
Requirements
- 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
- Strong written and verbal communication skills
Preferred qualifications
- Experience with reinforcement learning for large language models
- Experience studying training at scale: scaling behavior, training dynamics, or method development on large models
- Experience with large-scale distributed training systems
- Familiarity with LLM architectures and training methodologies
- Experience profiling and optimizing the performance of ML workloads
- Experience with RL environments, evaluations, or sandboxed code execution
- Experience with Rust or C++
Conditions
- Annual Salary: $500,000 - $850,000 USD
- Location-based hybrid policy: all staff expected in one of our offices at least 25% of the time
- Visa sponsorship available
- Minimum education: Bachelor's degree or equivalent