Staff+ Research Engineer, RL Data Platform

Anthropic•San Francisco, NY

7d•Hybrid

About The Position

Anthropic's RL Data Platform team builds the systems that produce, move, and serve the human data Claude learns from. This includes 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, such as human feedback, expert demonstrations, and graded transcripts. The team's goal is to make new data collection ideas feasible for researchers within a short timeframe. This is a full-stack, ownership-heavy role on a small, senior team. The engineer will design and ship web interfaces used by thousands of expert annotators, build the backend services and data pipelines behind them, and collaborate directly with RL researchers to understand their data needs. The role involves scoping projects, making architectural decisions, and managing them through to production. The team seeks engineers who prioritize researchers as users, focus on reliability, and value the quality of data output as much as the input UI.

Requirements

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.

Nice To Haves

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 evaluated.

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'.