Staff+ Software Engineer, RL Data Platform

Anthropic · San Francisco, CA | New York City, NYsenior

In short

▸Construyes plataformas full-stack para recolectar datos humanos que entrenan a Claude, con enfoque en confiabilidad y rapidez.
▸Trabajas directamente con investigadores de RL para transformar ideas en campañas de recolección de datos en menos de una semana.
▸El proyecto más destacado: hacer que el feedback humano llegue al entrenamiento de modelos en menos de 48 horas tras una idea.

Fluency in English required for collaboration with global teams.

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What they ask for

✓Experiencia en TypeScript/React y Python en producción.
✓Habilidad para liderar proyectos desde un boceto hasta producción.
✓Capacidad de colaborar con investigadores técnicos cuyas necesidades cambian constantemente.
✓Enfoque en confiabilidad, latencia y usabilidad de sistemas en uso continuo.
✓Uso efectivo de herramientas de IA en tu trabajo diario.
✓Conciencia de los impactos sociales de tu trabajo.

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TypeScriptReactPythonBackend servicesAPIsData pipelinesWeb interfacesMonitoring toolsDashboardsRLHF

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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 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. 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. Key responsibilities • Design, build, and operate the feedback and data collection interfaces used by human annotators, domain experts, and internal researchers. • Build and maintain the backend services, APIs, and pipelines that route model samples to humans and return structured feedback to training. • Own the reliability, latency, and usability of systems that run continuously against live model endpoints. • Partner with RL researchers to translate loosely specified data needs into well-scoped collection campaigns and the tooling to run them. • Build dashboards, monitoring, and inspection tools so researchers can see data quality and throughput without asking an engineer. • Identify and remove the bottlenecks between "we want this data" and "it's in the training mix". Minimum qualifications • Strong full-stack engineering skills, with production experience in TypeScript/React on the frontend and Python on the backend. • Experience designing and operating backend services and data pipelines that other teams depend on. • A track record of owning projects end-to-end, from an ambiguous brief to something in production that people use. • Comfort working directly with technical stakeholders whose needs change week to week, and the judgment to push back when something isn't worth building. • Effective use of AI tools in your own day-to-day work. • Care about the societal impacts of your work. Preferred qualifications • Experience building annotation, labelling, evaluation, or other human-in-the-loop data tooling. • Experience with RLHF, preference data, or other human-feedback pipelines for ML systems. • Experience shipping researcher-facing or other expert-facing internal tools people love: interviewing users, hunting down friction, measurably improving the experience. • Experience running experiments on data collection interfaces and using the results to improve data quality. • Experience working with crowdworker or expert vendor platforms at scale. • Familiarity with how LLMs are trained and eval

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