Software Engineer, RL Data

Anthropic Rl Data Engineer

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

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.

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: 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

Minimum Qualifications

• 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

Preferred Qualifications

• 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

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

The annual compensation range for this role is listed below.For sales roles, the range provided is the role's On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.

Annual Salary

$320,000—$485,000 USD

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.

We encourage you to apply even if you do not believe you meet every single qualification.

Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.

Your safety matters to us.

To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.

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.

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