🚀 Research Intern (MS/PhD, 6–12 months) – Robot Learning - Imitation Learning, Foundation Models, RL
LocationMountain View, CA (On-Site)/Internship
Work modeon-site
Typeinternship
DepartmentResearch
Company size11+ people
First seen2w ago
Last seen2d ago
All open roles
AI
🚀 Research Intern (MS/PhD, 6–12 months) – Robot Learning - Imitation Learning, Foundation Models, RL
Mountain View, CA (On-Site)/Internship
Hiring process
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About the role
Join our research team to work on learning-based control and perception for real-world robot manipulation. You’ll train and evaluate manipulation policies, build the data and evaluation infrastructure behind them, and run experiments that go all the way from an idea to a robot doing something new. We care more about your ability to train models that work than about any particular robot, task, or sensor you’ve used before.
Requirements
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01Currently enrolled in an MS or PhD program in Robotics, Computer Science, Machine Learning, or related field
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02Strong foundation in training neural networks — you can take a model from idea to a working, debugged result
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03Strong foundation in imitation learning or reinforcement learning
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04Proficiency in Python and at least one deep learning framework (PyTorch, JAX)
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05Experience with vision-based policy learning — diffusion policies, 3D policies, vision-language-action models, video action models
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06(+) Experience with robot simulators (e.g., MuJoCo, Isaac Gym/Lab)
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07(+) Hands-on experience with robot hardware, real-world data collection, or sim2real adaptation
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08(+) Familiarity with contact-rich or dexterous manipulation, or tactile sensing
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09(+) Publications or preprints at robotics or ML venues (CoRL, RSS, ICRA, NeurIPS, ICML, ICLR)
Details & responsibilities
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01Design, train, and evaluate manipulation policies on real hardware
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02Run focused research experiments end-to-end — form a hypothesis, build the ablation, and draw a clear conclusion from noisy real-world results