Software Engineer, RL Data

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.

This is a senior, foundational role on a new team: you'll make architecture decisions the rest of the team builds on, and help shape what we build first. The work is hands‑on and varied. Some weeks you will be deep in pipeline or infrastructure engineering; others you will be tuning prompts until output is good, or working with a research team that depends on your systems and shipping the fixes they need.

Anthropic’s RL Data team builds the systems that produce high‑quality reinforcement learning data for Claude: data collection pipelines, human feedback tooling, the execution environments RL tasks run in, and the quality assurance that keeps training data trustworthy  goal is to make Claude great at real work — especially the work that matters most, like AI safety research and beneficial deployments of AI.

(To be upfront: this is dual‑use work — it advances general capabilities too.)

• 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 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, and work with operations, security, and compliance partners to roll our systems out to new users and vendors.

• A track record of owning major projects end‑to‑end in fast‑paced, ambiguous environments — for example as a founder or CTO, forward‑deployed engineer, tech lead, founding engineer at a startup, or creator of a substantial open‑source project.
• Trusted to run key projects: you lead and inspire others, plan work streams effectively, collaborate with cross‑functional stakeholders, and proactively eliminate or escalate blockers.
• Strong software engineering skills in at least one modern programming language — we mostly use Python and Type Script, but care more that you pick new tools up quickly than that you know our exact stack. Familiarity with Docker, Kubernetes, and common cloud infrastructure is a plus.
• Effective use of AI tools in your own day‑to‑day work.
• Care about the societal impacts of your work.

• Experience with reinforcement learning on LLMs, particularly on the data side: creating evals, environments, rewards, graders, or training data.
• Experience helping organizations use AI more effectively, including integrating with third‑party tools via APIs, CLIs, and MCP servers.
• Strong data engineering skills: pipelines that handle large volumes reliably in production, LLM‑powered enrichment steps, and a focus on improving data quality.
• Experience shipping user‑facing products or internal platforms people love: interviewing users, hunting down friction, measurably improving the experience.
• Basic familiarity with AI safety or security research.

• Take a data collection pipeline from research prototype to a production service that serves many research teams — collection, human validation, grading, and everything in between.
• Own the program of developing sandboxed execution environments realistic enough for long‑horizon, high‑tool‑use agentic tasks — and harden them so they behave correctly across millions of rollouts in a frontier training run.
• Bring a new data source online — from first conversation with a partner organization to data flowing into production training runs — coordinating with product, security, privacy, legal, and infrastructure teams along the way.
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