Role focuses on world models and synthetic data for autonomous driving research.
Build, scale, and optimise GAIA-class architectures and data generation pipelines.
Bridge ML research with engineering for high-throughput generation and training.
Post-train world models for new embodiments and behaviours.
Improve throughput, yield, and self-serve synthetic data workflows.

🎯 Requirements

4+ years in applied ML / research engineering
Strong Python and PyTorch; GPU training and debugging
Hands-on with video, generative, or world models
Knowledge of cameras and 3D geometry
Experience using generated data in training and measuring impact
Ability to run generation/training at scale (multi-GPU)

🎁 Benefits

Hybrid work policy with London office time
Core working hours to suit you and your team
Culture focused on autonomy and high-trust teams

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