Machine Learning Engineer, Synthetic Data

Job summary

Responsibilities

Simulation is advancing end-to-end autonomous driving research. The team’s mission is to accelerate AV2.0 by incubating capabilities that become company-level advantages — generative world models and the synthetic data they produce are one of those.

• Model work: Post-train or ablate a GAIA rig-transfer or pose-transfer checkpoint (NVS warp, calibration/intrinsics, shortcut/distillation).
• Generation at scale: Kick off SDS / Flyte jobs for tens of thousands of segments; debug GPU capacity, KV cache, DDIM step count, MCAP/delta-table correctness.
• Landing in training: Binarise synthetic into a corpus-compatible table, wire sampling hooks, train RL and read suite + on-road diffs.
• Capability expansion: Pose-transfer ELK/AEB sets, dashcam→Alpha3, or a new OEM rig (Nissan Proto 2.1, Gen3, BMW).
• Cross-team: Driving-model owners on mix ratios, closed-loop/eval on whether generated MCAPs are usable as suites.

Tech stack

• Python
• PyTorch