Robotics Research Intern - Post-Training

Qualifications

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.

Interest in open research problems involving large-scale machine learning grounded in physical systems.

Proficiency in Python and a deep-learning framework such as PyTorch.

Ability to collaborate effectively with researchers and engineers and communicate research findings clearly.

Bonus Qualifications

Experience with pretrained generalist policies, foundation models, or large-scale robot-learning systems.

Familiarity with offline or online reinforcement learning, DAgger, interactive learning, or human-in-the-loop methods.

Experience with simulation, sim-to-real transfer, policy distillation, or robotic manipulation.

Experience with learned world models, model-based reinforcement learning, or planning.

Publication record or interest in publishing at leading venues such as CoRL, NeurIPS, ICLR, ICML, RSS, ICRA, IROS, or related conferences and journals.

Interest in translating fundamental research into reliable methods that can be evaluated on real robotic systems and practical downstream tasks.