Mountain View CA
Job Description:
>> Design and implement advanced robot learning architectures to support dexterous manipulation, path planning, and autonomous task sequencing
>> Develop end-to-end policy training pipelines, integrating multi-modal sensory data with control outputs
>> Build policy inference and closed-loop control that connect perception, planning, and execution on physical robotic platforms
>> Apply and extend large-scale architectures to embodied tasks, grounding, and sim-to-real adaptation
>> Collaborate with cross-functional teams to deploy robot policies on hardware, ensuring robustness, repeatability, and safety
>> Lead data strategy for demonstrations, teleoperation, simulation pipelines, and evaluation frameworks for manipulation policies
>> Stay current with embodied AI research and share insights internally through discussion, mentorship, and technical presentations
Requirements:
>> - Have you worked in robotics, robot learning, or embodied AI domain?
>> PhD in a relevant STEM field, or Masters with equivalent industry experience in robotics, robot learning, or embodied AI
>> Proven experience building and deploying machine learning models on robotic systems-including training, evaluation, and real-world execution or simulation
>> Deep understanding of modern AI architectures with strong experience training models at scale
>> Strong PyTorch implementation skills, including authoring custom modules, batching, debugging, and performance optimization
>> Practical experience with ROSROS2 and integrating learned policies into manipulation or motion control workflows
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