Research Scientist - Robot Learning (VLA / WAM)
Tasks
- Adapt VLM backbones for control with co training
- Build evaluation predicting real world transfer
- Close sim to real gap using domain randomization system identification and calibration
- Curate and weight training mix across robot datasets
- Design action representation and decoding
- Own VLA and WAM training pipeline end to end
- Perform supervised fine tuning and reinforcement learning for robustness
- Train policies for vision language action and world action models
- Train world model components for future observation prediction
Perks/Benefits
- N/A
Skills/Tech-stack
Action Representation | Calibration | Computer Vision | Control | Diffusion Models | Distributed Training | Domain Randomization | FSDP | Fine Tuning | Flow matching | Imitation Learning | Language Models | PyTorch | Python | Reinforcement Learning | Reinforcement learning fine tuning | Robot Learning | Supervised Fine Tuning | System Identification | Vision Language Models | Vision-language
Education
Roles
Regions
Countries
States
Cities
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