Staff Software Engineer (Code RL)

TL;DR

Staff Software Engineer (Code RL): Designing and scaling agentic coding environments and reinforcement learning infrastructure for Claude with an accent on API design and system reliability. Focus on building high-performance frameworks that enable researchers to iterate quickly while ensuring production RL systems are maintainable and robust.

Location: Hybrid (San Francisco, CA | New York City, NY | Seattle, WA). Must be in office at least 25% of the time

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

Company

Anthropic is a public benefit corporation dedicated to creating reliable, interpretable, and steerable AI systems that are safe and beneficial for society.

What you will do

• Design widely-used APIs, frameworks, and abstractions for engineers and researchers with a focus on legibility and principled defaults.
• Embed with research teams on a rotational basis to build supporting systems and then transfer ownership for long-term maintenance.
• Refactor and improve the reliability and structure of research codebases without hindering experimental velocity.
• Structurally prevent silent failure modes using type safety, targeted testing, and well-designed invariants.
• Enhance the reliability of production RL systems through monitoring, regression detection, and triage tooling.
• Establish engineering standards, design patterns, and review practices while mentoring the broader team.

Requirements

• Deep expertise in Python, specifically static typing, safe async, concurrency patterns, and performance optimization.
• Proven track record of designing intuitive and safe APIs or frameworks adopted by other teams.
• Experience working productively within large, evolving, or research-style codebases.
• Demonstrated ability to anticipate failure modes and prevent them through structural system design.
• Strong written and verbal communication skills for explaining complex designs to diverse collaborators.
• Must be based in or able to work from San Francisco, NYC, or Seattle to meet the 25% office presence requirement.

Nice to have

• Experience building infrastructure or frameworks for ML research or RL workflows.
• Familiarity with agentic systems, LLM training pipelines, or reinforcement learning concepts.
• Experience with large-scale distributed systems or dataset lifecycle management.
• Experience building client libraries for sandboxed or remote execution platforms.
• Prior experience as a technical lead or maintainer of an open source project.

Culture & Benefits

• Competitive compensation package with optional equity donation matching.
• Generous vacation and parental leave policies.
• Flexible working hours and access to high-quality collaborative office spaces.
• Collaborative "big science" environment focusing on high-impact, long-term AI safety goals.
• Visa sponsorship availability for eligible candidates.