Sr. Physical AI Research Scientist
Tasks
- Apply domain randomization
- Apply self supervised learning
- Build robot simulation frameworks
- Collaborate with academic and industry partners
- Deploy robot agents on physical robots
- Design imitation learning algorithms
- Design robot safety architecture
- Design safety aware robotic manipulation solutions
- Develop multimodal learning algorithms
- Develop offline reinforcement learning
- Develop reinforcement learning algorithms
- Develop safe exploration and continual learning
- Develop safety verification and monitoring modules
- Develop world models
- Evaluate sim-to-real transfer
- Fine tune policy steering and best of sampling
- Fine tune robotic foundation models
- Implement Vision-Language Action Models
- Improve robustness to noise and distribution shift
- Mentor junior team members
- Perform data collection and training pipeline development
- Translate research concepts into prototypes
Perks/Benefits
Skills/Tech-stack
AI alignment | Artificial Intelligence | Computer Vision | Constitutional AI | Continual Learning | Contrastive Learning | Diffusion Models | Domain Randomization | Flow matching | Flow-Matching Models | Foundation Models | Imitation Learning | Isaac Sim | JAX | Language Models | Language Processing | Machine Learning | Matching models | Meta Learning | Mujoco | Multimodal Learning | Natural Language | Natural Language Processing | Offline Reinforcement Learning | PyTorch | ROS1 | ROS2 | Reinforcement Learning | Reward Modeling | Robotic Manipulation | Robotic navigation | Robotics | Self-Supervised Learning | Self-supervised | Sim-to-Real | Simulation | Supervised Learning | Transfer Learning | Vision Language Models | Vision-language | Vision-language-action | World Models
Education
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