Research Engineer, RL Engineering

The shape of it

In the posting’s own words

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.

What it asks for · 7

• 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

Also a plus

• 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
• Experience with Rust or C++

What the job covers

• 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

Tools and skills named

Words the posting leans on

• training13×
• experience10×
• system10×
• research8×
• run5×
• production4×
• scale4×
• build3×
• code3×
• evaluation3×
• improve3×
• large3×
• models3×
• reinforcement learning3×
• distributed2×
• engineering2×

Counted from the posting after the mission statement and the legal notices are set aside. The ones near the top are the ones a screener is looking for.

More open at Anthropic

• 3P Revenue Accounting & ReportingSan Francisco, CA | Seattle, WA
• Account Executive, AI NativeNew York City, NY; San Francisco, CA | New York City, NY
• Account Executive - DNBSingapore
• Account Executive, NonprofitSan Francisco, CA | New York City, NY
• Account Executive - Public Sector (ASEAN)Singapore
• Account Executive, StartupsDublin, IE