Robotics Intern, Post-Training and Continual Learning

What you'll do

• Develop post-training methods to adapt pretrained robot policies.
• Design and conduct experiments in simulation and, where appropriate, on real robotic systems.
• Analyze and communicate findings, contributing to research publications and internal technical reports.

What they ask for

• Currently pursuing a Ph.D. in Computer Science, Machine Learning, Robotics, or a related field.
• Research experience in robot learning, reinforcement learning, imitation learning, generative modeling, world models, or a related area.
• Proficiency in Python and a deep-learning framework such as PyTorch.
• Ability to collaborate effectively and communicate research findings clearly.

Nice to have

• Experience with pretrained generalist policies, foundation models, or large-scale robot-learning systems.
• Familiarity with reinforcement learning, DAgger, human-in-the-loop methods, simulation, sim-to-real transfer, policy distillation, or world models.

Pay

$45 and $65/hour (USD, base pay; California-based roles)

Tools

Python, PyTorch

AI focus

Research and develop post-training and continual adaptation methods for pretrained generalist robot policies, including reinforcement learning, imitation and human-in-the-loop learning, simulation, world models, policy distillation, and large-scale robot learning.