Software Engineer, RL Data

What you’ll work on

Turning a capability gap into thousands of verifiable tasks — for example, planting subtle bugs deep in a real project's dependencies for the agent to root-cause.

Building environments that behave like production: real codebases with their real dependencies, and simulations of apps like email, calendar, and docs that match the real ones down to search, errors, and pagination.

Designing graders for open-ended work that separate good solutions from bad without penalizing valid alternatives or rewarding shortcuts.

Building automated checks that catch reward hacking, leaked answers, broken environments, and paths the real product wouldn't allow, before data reaches training.

Scaling from a thousand hand-checked tasks to millions, with filters and judges you can trust in place of a person reading each one

You may be a fit if

You have strong software engineering fundamentals, and you've built non-trivial systems in Python: data pipelines, test or eval infrastructure, sandboxes, or backend services.

You have taste for what makes an environment trustworthy: faithful enough that skills transfer, strict enough that shortcuts fail, and simple enough to debug when a result looks wrong.

You are careful and systematic — understanding both aggregate metrics and close reading of raw outputs.

You enjoy turning messy, open-ended problems into repeatable, high-quality data production systems.

RL or ML experience is a plus but not required. We welcome strong engineers who want to do research on what makes models better.

Applying

If there appears to be a fit, we'll reach out to schedule 2-3 short technicals. After, we'll schedule an onsite in our office, where you'll work on a small project, discuss ideas, and meet the team.