Synthetic Data Research Engineer — Shape Model Capabilities

Cursor; San Francisco (CA); On-site; USD 150,000 - 210,000; Full time

Job summary

SpaceXAI is seeking a Research Engineer for Synthetic Data to invent and scale datasets that teach models new capabilities. You own the full loop from idea to validated data production, building reliable pipelines and verifiers and confirming data improves model performance.

You will design diverse data recipes, run ablations, and collaborate with research and other RL Data teams to translate capability goals into concrete data plans.

Qualifications

• You have research experience on synthetic or post‑training data, in industry or academia.
• 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.

Responsibilities

• Design end-to-end synthetic data recipes that turn raw sources into large, diverse, verifiable task sets.
• Build verification and anti‑hacking mechanisms so rewards are simple to compute and hard to game.
• Control difficulty and diversity so data stays learnable and reflects real usage.
• Run targeted ablations and analyses that show a dataset improves capabilities and rule out data artifacts.
• Iterate from traces and training results to close gaps and turn successful recipes into reusable infrastructure.
• Partner with research and RL Data teams to translate model capability goals into concrete data plans.

Skills

Research experience

Software engineering fundamentals

Job description

SpaceXAI is seeking a Research Engineer for Synthetic Data to invent and scale datasets that teach models new capabilities. You own the full loop from idea to validated data production, building reliable pipelines and verifiers and confirming data improves model performance.

You will design diverse data recipes, run ablations, and collaborate with research and other RL Data teams to translate capability goals into concrete data plans.