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.

About the role

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'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 experienced engineers who own outcomes end-to-end — down to reading transcripts, supporting users, and wrangling vendors.

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 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.)

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

Minimum qualifications

• 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 workstreams 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 TypeScript, 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.

Preferred qualifications

• 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.

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: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.

Come work with us!

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.