Anthropic Research Engineer at Anthropic focusing on building and improving the core reinforcement learning (RL) training system. Collaborate with research teams to study RL training behavior and implement new training methods.
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. The team works closely with research teams across the company on the science and engineering of making RL work at scale.
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.
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
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
Qualification
Experience studying training at scaleExperience with RL environmentsExperience with Rust or C++
Required
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
Care about the societal impacts of your work and are committed to developing safe and beneficial systems
Preferred
Experience with reinforcement learning for large language models, in research, production, or both
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 working close to a frontier training run
Experience profiling and optimizing the performance of ML workloads
Experience with RL environments, evaluations, or sandboxed code execution
