Staff+ Software 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 signals, and the tooling researchers use to launch, monitor, and inspect data collection. The team's goal is to ensure a steady supply of high-quality data for every RL run, enabling researchers to collect new data types quickly and integrate them into the training mix efficiently. This is a full-stack, ownership-heavy role on a small, senior team, involving the design and implementation of web interfaces for expert annotators, backend services, data pipelines, and close collaboration with RL researchers to understand and fulfill their data needs. The role requires engineers who prioritize reliability, treat researchers as users, and focus on the quality of the data produced.

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".