Research Scientist Intern, Robotics Foundation Models Post-Training (PhD) Responsibilities

Develop scalable offline RL algorithms for post-training WAMs and VLAs using large, heterogeneous robot datasets.
Investigate reward learning, value estimation, and policy optimization for generative action models, including approaches that use learned world models to support policy improvement.
Address challenges in learning from mixed-quality experience, including distribution shift, sparse rewards, and long-horizon credit assignment.
Build efficient training and data pipelines, and study how policy performance scales with dataset size, model capacity, and compute.
Design experiments and benchmarks to evaluate policy improvement and generalization across tasks and environments.
Collaborate with research scientists and engineers to evaluate learned policies in simulation and on real robots.
Communicate research findings and publish results at leading machine learning and robotics venues.

Minimum Qualifications

Currently has or is in the process of obtaining a Ph.D. degree in Computer Science, Machine Learning, Artificial Intelligence, Robotics, or a related STEM field
Research experience in reinforcement learning, including policy optimization, value learning, or offline/off-policy learning
Experience implementing and training deep learning models using Python and PyTorch or a comparable framework
Experience designing controlled experiments, analyzing results, and debugging machine learning algorithms
Interpersonal experience: cross-group and cross-culture collaboration
Must obtain work authorization in country of employment at the time of hire and maintain ongoing work authorization during employment

Preferred Qualifications

Intent to return to degree program after the completion of the internship/co-op
Experience developing offline RL methods, particularly for continuous control, large datasets, or learning from suboptimal experience
Experience training or post-training VLAs, WAMs, or other foundation models for embodied decision-making
Experience with diffusion or flow-based policies, action-sequence prediction, model-based RL, or action-conditioned world models
Hands-on experience with distributed training across multiple GPUs or nodes, including improving training throughput, memory efficiency, and stability
Experience evaluating learned policies on physical robots or robotics simulation benchmarks
Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, and/or accepted papers in top conferences or journals such as NeurIPS, ICML, ICLR, CoRL, RSS, ICRA, CVPR, ICCV, ECCV, ACL, etc

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