Research Engineer, Synthetic Data

What you’ll work on

Designing end-to-end synthetic data recipes that turn raw sources (traces, documents, codebases, workflows) into large, diverse, verifiable task sets.

Building strong verification and anti-hacking mechanisms so rewards are simple to compute and hard to game.

Controlling difficulty and diversity so data stays learnable and reflects real usage instead of collapsing into templates.

Running targeted ablations and analyses that demonstrate a dataset caused a capability improvement (and ruling out grader or data artifacts).

Iterating from traces and training results to close specific gaps, then turning successful one-off recipes into reusable infrastructure.

Partnering with research and other RL Data teams to translate model capability goals into concrete synthetic data plans.

You may be a fit if

You have research experience on synthetic or post-training data, in industry or academia. You've designed a data generation method, shipped the data into training, and can show its impact to the model you’re training.

You have strong software engineering fundamentals and have built non-trivial data pipelines, agent systems, or evaluation frameworks.

You have taste for what makes a good training task: hard enough to teach something real, simple enough to grade reliably, and resistant to reward hacking.

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.

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.