Software Engineer, Anthropic

• 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
• Bachelor’s degree or an equivalent combination of education, training, and/or experience
• We encourage you to apply even if you do not believe you meet every single qualification

Desirable

• 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 at scale
• Our goal is to make Claude genuinely great at complex, real-world work — and to point those capabilities at the things that matter most, including AI safety research and beneficial deployments of AI. (To be upfront: this is dual-use work — it advances general capabilities too, though we aim to differentially advance the beneficial ones.)
• This is a foundational role on a new team: you'll help shape our technical direction and what we build first
• The work is hands-on and varied
• Some weeks you'll be deep in pipeline or infrastructure engineering; others you'll be tuning prompts until the output is good, or sitting with a research team that depends on your systems and shipping the fixes they need
• We're looking for strong engineers who will also do whatever else it takes to make their systems succeed — reading transcripts, supporting users, and wrangling vendors
• 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
• Representative projects
• Take QA checks that a model has learned to game, and make them hold up under heavy optimization pressure
• Build a review flow that lets a busy expert check an RL task in under five minutes
• Cut the time from 'rough task idea' to 'QA-passed RL task' from days to hours
• Sit for a week with a team that uses our platform, then ship the fixes that help them most
• Harden a sandboxed environment so tasks behave correctly across millions of rollouts
• Onboard a new data vendor, and fix the rough edges they hit