Staff Software Engineer, Code RL en Anthropic

Presencial - San Francisco, CA

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The Staff Software Engineer, Code RL will lead the engineering effort behind Anthropic’s reinforcement learning capabilities for code generation. Working closely with research teams, you will design and build robust APIs, frameworks, and infrastructure that enable rapid experimentation, maintain high reliability of production RL runs, and set engineering standards for a new team.

Salary

USD 405,000 - 625,000

Requirements

Skills

  • Deep expertise in Python, including static typing, safe async and concurrency patterns, and writing performant Python code
  • A track record of designing intuitive, safe APIs or frameworks that other engineers or teams adopted and built on
  • Experience working productively in large, evolving, or research-style codebases that you didn't originally write
  • Demonstrated ability to anticipate failure modes — especially silent ones — and prevent them structurally through system design, type safety, and testing
  • Strong written and verbal communication skills, including the ability to explain system designs to collaborators with varied engineering backgrounds
  • Comfort with ambiguity: able to scope your own work from a loosely defined problem and drive it to a maintainable outcome

Responsibilities

  • Design widely-used APIs, frameworks, and abstractions that other engineers and researchers build on, with careful attention to interface legibility and principled defaults
  • Embed with research teams on a rotational basis: understand their engineering needs, build systems and APIs that support their work, and transfer ownership so teams can maintain those systems after you rotate off
  • Work directly in research codebases, improving reliability and structure without slowing down the research they support
  • Anticipate silent failure modes and prevent them structurally through type safety, well-designed invariants, targeted testing, and refactors that shrink the surface area for bugs
  • Contribute to the reliability of production RL systems, including monitoring, regression detection, and triage tooling
  • Help define engineering standards, review practices, and design patterns for a new team, and mentor researchers and engineers in adopting them

Technologies

PythonStatic typingAsync and concurrency patternsType safetyTestingAPI designFramework designSandboxed executionDistributed systemsLarge-scale data processingML researchReinforcement learning

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