Staff+ Research Engineer, RL Data Platform

The shape of it

In the posting’s own words

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.

What it asks for · 6

• 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.

Also a plus

• 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.

What the job covers

• 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".

Tools and skills named

Words the posting leans on

• data30×
• experience16×
• build13×
• researchers12×
• feedback11×
• interfaces10×
• pipelines10×
• expert8×
• model8×
• quality8×
• annotators7×
• data collection7×
• human7×
• systems6×
• tools6×
• backend services5×

Counted from the posting after the mission statement and the legal notices are set aside. The ones near the top are the ones a screener is looking for.

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