Research Engineer, Synthetic Data

About the Role

As a Research Engineer on the Synth Data team at SpaceXAI, you’ll invent and scale the synthetic data that teaches our models new capabilities. You own the full loop: spotting a capability the model is missing, designing a data generation strategy, building reliable pipelines and verifiers, and validating that the resulting data actually moves the model.

You form hypotheses about what data will help, build the systems to produce it at scale, run ablations, and read the resulting traces and training signals to understand why it worked (or didn’t). Your datasets go directly into frontier training runs, and shape the development of Grok models.

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.

Qualifications

• 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.
• Strong software engineering fundamentals and experience building non-trivial data pipelines, agent systems, or evaluation frameworks.
• Taste for what makes a good training task: hard enough to teach something real, simple enough to grade reliably, and resistant to reward hacking.
• Careful and systematic approach to both aggregate metrics and close reading of raw outputs.
• Passion for turning messy, open-ended problems into repeatable, high-quality data production systems.

Interview Process

• 2–3 short technical interviews
• Onsite in office: work on a small project, discuss ideas, and meet the team