Software Engineer, RL Data

Anthropic•Seattle, WA

2d•$320,000 - $485,000•Hybrid

About The Position

Anthropic's RL Data team builds the systems that produce high-quality reinforcement learning data for Claude. This includes data collection pipelines, human feedback tooling, execution environments for RL tasks, and quality assurance systems to ensure trustworthy training data at scale. The team's goal is to enhance Claude's capabilities for complex, real-world tasks, with a focus on AI safety research and beneficial AI deployments. This is a foundational role on a new team, offering the opportunity to shape technical direction and initial projects. The work is hands-on and varied, involving pipeline and infrastructure engineering, prompt tuning, and supporting research teams. The role requires engineers who are willing to go beyond core engineering tasks, including reading transcripts, supporting users, and managing vendors.

Requirements

Strong software engineering skills and proficiency in at least one modern programming language — we mostly use Python and TypeScript, and care more that you pick new tools up quickly than that you know our exact stack
Experience designing, building, and running backend systems or infrastructure
Effective use of AI tools in your own day-to-day work
Willingness to own problems end-to-end, including the parts that aren't engineering
Proactive, open communication: you can be trusted to run a workstream, and to escalate early when something's off
Comfort iterating quickly in ambiguous, fast-changing situations
Care about the societal impacts of your work

Nice To Haves

Experience building LLM-powered systems: prompt pipelines, evals, or products with models in the loop
Experience with reinforcement learning on LLMs: creating environments, rewards, graders, or training data
Time as a forward deployed engineer, founder, or early startup engineer — roles where you owned the outcome, not just the code
Experience shipping user-facing products, or internal platforms people love: interviewing users, hunting down friction, measurably improving the experience
Experience building data pipelines or integrations that move, transform, and index data from many sources
Experience building connectors or integrations with third-party tools and APIs, such as MCP servers
Experience with containers, Kubernetes, or simulation infrastructure
Experience handling sensitive data or working under tight security controls
Experience working with external data vendors
Basic familiarity with AI safety or security research

Responsibilities

Own significant parts of our stack end-to-end, from technical architecture through the unglamorous operational work that makes it succeed
Build data collection pipelines, read the transcripts they produce, and iterate on prompts, evals, and graders until the output is good
Develop and improve QA frameworks to catch reward hacking and ensure environment quality
Build interfaces that make collecting human data fast and painless for the people providing it
Harden execution environments — sandboxing, snapshotting, tool coverage — so tasks hold up at training scale
Embed with the teams and domain experts who use our systems day-to-day: design pipelines and evals with them, support them directly, and ship the improvements they need
Work with operations, security, and compliance partners to roll our systems out to new users, and manage technical relationships with external data vendors