Experience Fresher-friendly - open to freshers / early-career candidates
About the role
structured by ORIAll open roles AI ### 🚀 Research Intern (MS/PhD, 6–12 months) – Robot Learning - Imitation Learning, Foundation Models, RL Mountain View, CA (On-Site)/Internship Apply now Share ### About the role Join our research team to work on learning-based control and perception for real-world robot manipulation. You'll train…
What you will do
- Design, train, and evaluate manipulation policies on real hardware
- Run focused research experiments end-to-end — form a hypothesis, build the ablation, and draw a clear conclusion from noisy real-world results
- Integrate multimodal sensor streams (RGB, depth, proprioception, tactile) into policy architectures
- Contribute to data collection tools and replay infrastructure for learning from human demonstrations
- Work closely with hardware, AI, and perception engineers to close the loop from sensing to control
What they are looking for
- Currently enrolled in an MS or PhD program in Robotics, Computer Science, Machine Learning, or related field
- Strong foundation in training neural networks — you can take a model from idea to a working, debugged result
- Strong foundation in imitation learning or reinforcement learning
- Proficiency in Python and at least one deep learning framework (PyTorch, JAX)
- Experience with vision-based policy learning — diffusion policies, 3D policies, vision-language-action models, video action models
- (+) Experience with robot simulators (e.g., MuJoCo, Isaac Gym/Lab)
- (+) Hands-on experience with robot hardware, real-world data collection, or sim2real adaptation
- (+) Familiarity with contact-rich or dexterous manipulation, or tactile sensing
- (+) Publications or preprints at robotics or ML venues (CoRL, RSS, ICRA, NeurIPS, ICML, ICLR)
Benefits
- Paid internship with competitive compensation
- Work on cutting-edge problems in robot learning and manipulation
- Mentorship from researchers and engineers working at the frontier of embodied intelligence
- Access to real robot hardware and large-scale robot datasets
- Opportunity to publish or contribute to high-impact research alongside product-driven development
robot learningimitation learningreinforcement learningneural network trainingvision-based policy learningrobot hardwarereal-world data collectionsim2real adaptationPythonPyTorchJAXMuJoCoIsaac Gym/Lab
Full posting text
All open roles
AI
🚀 Research Intern (MS/PhD, 6–12 months) – Robot Learning - Imitation Learning, Foundation Models, RL
Mountain View, CA (On-Site)/Internship
Apply now Share
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
- 01Currently enrolled in an MS or PhD program in Robotics, Computer Science, Machine Learning, or related field
- 02Strong foundation in training neural networks — you can take a model from idea to a working, debugged result
- 03Strong foundation in imitation learning or reinforcement learning
- 04Proficiency in Python and at least one deep learning framework (PyTorch, JAX)
- 05Experience with vision-based policy learning — diffusion policies, 3D policies, vision-language-action models, video action models
- 06(+) Experience with robot simulators (e.g., MuJoCo, Isaac Gym/Lab)
- 07(+) Hands-on experience with robot hardware, real-world data collection, or sim2real adaptation
- 08(+) Familiarity with contact-rich or dexterous manipulation, or tactile sensing
- 09(+) Publications or preprints at robotics or ML venues (CoRL, RSS, ICRA, NeurIPS, ICML, ICLR)
Details & responsibilities
- 01Design, train, and evaluate manipulation policies on real hardware
- 02Run focused research experiments end-to-end — form a hypothesis, build the ablation, and draw a clear conclusion from noisy real-world results
- 03Integrate multimodal sensor streams (RGB, depth, proprioception, tactile) into policy architectures
- 04Contribute to data collection tools and replay infrastructure for learning from human demonstrations
- 05Work closely with hardware, AI, and perception engineers to close the loop from sensing to control
Compensation & benefits
- 01Paid internship with competitive compensation
- 02Work on cutting-edge problems in robot learning and manipulation
- 03Mentorship from researchers and engineers working at the frontier of embodied intelligence
- 04Access to real robot hardware and large-scale robot datasets
- 05Opportunity to publish or contribute to high-impact research alongside product-driven development
Interested in this role?
Applying takes a few minutes — you'll need an account to submit.
Apply now