About the role

Code RL at Anthropic drives reinforcement learning efforts behind Claude's coding capabilities, creating and scaling agentic coding environments. This is an engineering role with unusual latitude to set technical direction and standards.

You'll be part of a team solving the engineering side of research efforts such as embedding with research teams, getting up to speed on their systems and needs, and designing the frameworks, APIs, and infrastructure that let researchers move faster, then rotating off, leaving behind well-oiled systems those teams can understand, own, and maintain themselves. Your remit also includes the ongoing health of production RL runs: maintainable, monitored, and straightforward to triage.

The team's problem space spans the client side of sandboxed execution for agentic RL environments, large-scale data processing jobs, the lifecycle of production datasets, and the frameworks researchers build environments on. You won't own all of this yourself, you'll take on the slices where your depth matters most. You'll be a strong fit if you have deep expertise in Python, a refined sense of taste for API and framework design, and hard-won intuition for how complex systems fail — especially silently. You should be comfortable diving into messy research code, finding the load-bearing abstractions, and improving them incrementally while researchers continue to build on top of your work.

Key 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

Minimum qualifications

• 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

Preferred qualifications

• Experience building infrastructure, tooling, or frameworks for machine learning research or RL workflows
• Familiarity with reinforcement learning concepts, agentic systems, or LLM training pipelines
• Experience building or operating large-scale distributed systems
• Experience building client libraries or SDKs on top of sandboxed, containerized, or remote execution platforms
• Experience with large-scale data processing or dataset lifecycle management
• Experience designing plugin systems or extensible class hierarchies used across an organization
• Experience embedding with or consulting for other teams, including successfully handing off systems for others to own
• Experience defining code standards, lint rules, or static verification approaches adopted across multiple teams
• Prior experience as a technical lead, or setting engineering standards for a team
• Prior experience maintaining an open source project

Representative projects

These are examples of the challenges the team tackles; no one person will work on all of them:

• Design a base RL environment abstraction general enough to be subclassed across a wide range of environments
• Design a model-tool interface for sandboxed agentic environments that has explicit serialization semantics
• Partner with the platform teams that own the sandbox runtime to specify low-level features that improve the integrity of agentic coding tasks
• Design probes that catch sandbox regressions early
• Design the lifecycle and maintenance scheme for a production dataset
• Lead a research code refactor replacing loosely structured data containers with equivalents that carry stronger correctness guarantees, without breaking the experiments that depend on them
• Design lint rules and code-style requirements that favor statically verifiable patterns — including patterns less likely to be overlooked by an LLM reviewing or writing the code — to shrink the surface area for silent bugs
• Build the access layer that lets researchers discover and reuse data artifacts across teams