Paid Research Intern — Robot Learning & Manipulation

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

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.