Research Intern – Robot Learning - Imitation Learning, Foundation Models, RL (MS/PhD, 6–12 months)

Proception Inc.; Mountain View (CA); Hybrid; USD 41,000 - 55,000; Part time

Job summary

Proception Inc. is seeking a Research Intern (MS/PhD) to advance robot learning through imitation learning, foundation models, and RL. You will train and evaluate manipulation policies, build data/evaluation infrastructure, and run experiments from concept to real-world results.

You will collaborate with hardware, AI, and perception engineers, gain access to real robot hardware and large datasets, and have opportunities to publish or contribute to high-impact research.

Qualifications

• MS or PhD student in Robotics, CS, ML, or related field.
• Strong foundation in training neural networks from idea to working result.
• Experience with imitation learning or reinforcement learning.
• Proficiency in Python and at least one DL framework (PyTorch or JAX).
• Experience with vision-based policy learning and related models.

Responsibilities

• Design, train, and evaluate manipulation policies on real hardware.
• Run end-to-end experiments, form a hypothesis, and draw conclusions from noisy data.
• 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.
• Opportunity to publish or contribute to high-impact research.

Skills

Neural network training

Experiment design

Imitation learning

Reinforcement learning

Vision-based policy learning

Education

MS/PhD in Robotics/CS/ML

Tools

Python

PyTorch

JAX

Job description

Research Intern (MS/PhD, 6–12 months) – Robot Learning - Imitation Learning, Foundation Models, RL

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

• 01 Currently enrolled in an MS or PhD program in Robotics, Computer Science, Machine Learning, or related field
• 02 Strong foundation in training neural networks — you can take a model from idea to a working, debugged result
• 03 Strong foundation in imitation learning or reinforcement learning
• 04 Proficiency in Python and at least one deep learning framework (PyTorch, JAX)
• 05 Experience 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

• 01 Design, train, and evaluate manipulation policies on real hardware
• 02 Run focused research experiments end-to-end — form a hypothesis, build the ablation, and draw a clear conclusion from noisy real-world results
• 04 Contribute to data collection tools and replay infrastructure for learning from human demonstrations
• 05 Work closely with hardware, AI, and perception engineers to close the loop from sensing to control
• 01 Paid internship with competitive compensation
• 02 Work on cutting-edge problems in robot learning and manipulation
• 03 Mentorship from researchers and engineers working at the frontier of embodied intelligence
• 04 Access to real robot hardware and large-scale robot datasets
• 05 Opportunity to publish or contribute to high-impact research alongside product-driven development