ML Engineer, Manipulation
Tasks
- Analyze field performance and identify failure modes
- Build manipulation training pipelines
- Design evaluation metrics and regression tests
- Develop learning based manipulation models
- Develop sim-to-real workflows
- Drive iterative improvements through data collection and retraining
- Integrate policies with control and safety systems
- Optimize and distill models for edge deployment
Perks/Benefits
- N/A
Skills/Tech-stack
Behavior Cloning | Behavior Modeling | Computer Vision | Critical Systems | Data Augmentation | Diffusion Models | Distributed Training | Domain Randomization | Evaluation metrics | Machine Learning | ONNX | Performance Profiling | PyTorch | Python | Quantization | Robotics | Safety-Critical | Safety-critical Systems | Sim-to-Real | TensorRT | Training Pipeline | Transformer | Vision-language | Vision-language-action
Education
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