The opportunity
The data behind a model shapes it more than almost anything else. On the RL Data team at SpaceXAI, you decide what that data is for our agents: which problems they practice, what world they practice in, and how they're judged.
What you'll do
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 have strong software engineering fundamentals, and you've built: non-trivial systems in Python: data pipelines, test or eval infrastructure, sandboxes, or backend services.
What they're looking for
- 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.
