Staff Software Engineer, Code RL

Job Application for Staff Software Engineer, Code RL at Anthropic

Back to jobs New Staff Software Engineer, Code RL San Francisco, CA | New York City, NY | Seattle, WA

About Anthropic

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

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

The annual compensation range for this role is listed below.

For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.

Annual Salary: $405,000 - $625,000 USD

Logistics

Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience

Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience

Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position

Location-based hybrid policy:...