Machine Learning Engineer (Synthetic Data)
Tasks
- Diagnose geometry and calibration failures
- Expand coverage to new vehicle platforms and safety scenarios
- Land synthetic data in driving model training
- Optimize inference throughput and yield
- Own generation loop for synthetic data
- Partner on generation evaluation and training integration
- Post train and iterate world models
Perks/Benefits
Skills/Tech-stack
3D Geometry | Autoregressive Video | Behaviour Cloning | Calibration | Camera Geometry | Camera systems | Data Lineage | Dataset Management | Diffusion Models | Distributed Training | Extrinsics | Few Step Sampling | Flow matching | GPU Computing | GPU Inference | Image Reprojection | Intrinsics | KV cache | Knowledge Distillation | Multi Camera | Multi-camera systems | Multimodal Learning | Neural Rendering | Novel View Synthesis | Offline Reinforcement Learning | PyTorch | Python | Quality Filtering | Reinforcement Learning | Reward Models | Video Generation | View synthesis | Workflow Orchestration | World Models
Education
N/A
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