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Research Engineer, Code RL (Reinforcement Learning)

Anthropic· AI Research & Engineering
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Hybrid (San Francisco, CA), Hybrid (New York City, NY)$500k - $850kFull-timePosted 4mo ago

About the role

Research Engineer role on the Code RL team within Anthropic's RL organization. Focuses on enabling models to write, edit, test, debug, and ship real software end-to-end on real codebases with real tools, combining research and engineering to design RL environments, reward signals, verifiers, and training experiments for code-related capabilities.

Responsibilities

  • Design RL environments and coding tasks, build reward signals and verifiers that define what 'good code' means
  • Run training experiments on frontier models, diagnose why models improve (or not) on software-engineering tasks
  • Improve speed and reliability of pipelines for iterative research and deployment
  • Develop and work on Code RL focus areas from agentic coding behaviors to high-performance code for accelerators
  • Own end-to-end systems and debugging across the stack
  • Ensure code quality, testing, and performance

Qualifications

  • Strong software-engineering skills and deep Python expertise, including async/concurrent programming
  • Comfortable owning systems end to end and debugging across the stack
  • Ability to balance research exploration with engineering implementation and interpret experimental results
  • Care about code quality, testing, and performance
  • Passionate about the potential impact of AI and safe, beneficial systems

Skills

Required:PythonSoftware engineeringSystem debugging

Benefits

  • Competitive compensation and benefits
  • Optional equity donation matching
  • Generous vacation and parental leave
  • Flexible working hours
  • Lovely office space and collaboration environment
Last updated Jul 8, 2026. Interested? Apply on the company site.

About Anthropic

building reliable, interpretable, and steerable AI systems

Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.

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