As our AI and Foundation Model Reseach Scientist, you will build Physical AI systems for humanoid robots that assist people in home environments.
Your mission is to establish a continuous improvement cycle between real-world data collection and perception-decision-action models, creating AI that's safe, practical, adaptive, dexterous, and robust.
RequirementsExperience building AI systems that perform in real-world environments through experimentation with world models or foundation models
Ability to lead the design and validation of continuous improvement cycles: real-world data → model → action → feedback
Experience developing and operating production-quality libraries and systems in C/C++, Python, Docker, Linux, and Git
Bonus Qualifications
Experience implementing force control, compliance control, and bilateral control in real-world environments
Experience creating or making significant contributions to widely-used open-source projects
Experience designing large-scale multimodal data pipelines for acquisition, synchronization, and preprocessing
Experience designing algorithms for behavior models, such as stable learning, safe exploration, and online adaptive control
Experience bridging technology development and social acceptance through design and real-world implementation
Experience with distributed training, optimization, and hyperparameter tuning of billion-parameter-scale deep learning models
Understanding of theory and implementation for cutting-edge methods including representation learning, world models, imitation learning, and reinforcement learning
Deep understanding of humanoid mechanics, actuator control, and sensor integration
Experience developing systems applying deep learning to whole-body motion control, locomotion, and manipulation
Strong communication skills to drive cross-disciplinary collaboration
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