About the role
<div class="content-intro"><h2><strong>About Anthropic</strong></h2> <p>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.</p></div><h2><strong>
About
the role</strong></h2> <p>Anthropic's RL Data Platform team builds the systems that produce, move, and serve the human data Claude learns from: the interfaces humans use to give feedback, the pipelines that turn raw feedback into training signal, and the tooling researchers use to launch, monitor, and inspect data collection. Every RL run depends on a steady supply of high-quality data - human feedback, expert demonstrations, graded transcripts - and when a researcher has an idea for new data on Monday, our job is to make it collectable by Wednesday and in the training mix by Friday.</p> <p>This is a full-stack, ownership-heavy role on a small, senior team. You'll design and ship web interfaces used by thousands of expert annotators, build the backend services and data pipelines behind them, and work directly with RL researchers to understand what data they need and why. You'll scope your own projects, make architectural calls, and see them through to production. We're looking for engineers who treat researchers as their users, build for reliability first, and care as much about the shape of the data leaving the system as the UI going into it.</p> <h2><strong>Key
responsibilities</strong></h2> <ul> <li> <p>Design, build, and operate the feedback and data collection interfaces used by human annotators, domain experts, and internal researchers.</p> </li> <li> <p>Build and maintain the backend services, APIs, and pipelines that route model samples to humans and return structured feedback to training.</p> </li> <li> <p>Own the reliability, latency, and usability of systems that run continuously against live model endpoints.</p> </li> <li> <p>Partner with RL researchers to translate loosely specified data needs into well-scoped collection campaigns and the tooling to run them.</p> </li> <li> <p>Build dashboards, monitoring, and inspection tools so researchers can see data quality and throughput without asking an engineer.</p> </li> <li> <p>Identify and remove the bottlenecks between "we want this data" and "it's in the training mix".</p> </li> </ul> <h2><strong>Minimum
