Staff+ Software Engineer, RL Data Platform at Anthropic

Hybrid - San Francisco, CA | New York City, NY

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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. This full‑stack, ownership‑heavy role involves designing and shipping web interfaces, building backend services and data pipelines, and working directly with RL researchers to understand data needs. Engineers should treat researchers as users, prioritize reliability, and care about the shape of the data leaving the system as well as the UI going into it.

Salary

USD 320,000 - 405,000

Requirements

Skills

  • 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.

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".

Technologies

TypeScriptReactPython

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