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

Interns in this role will conduct fundamental and applied research to advance foundation models for robotics, with a focus on mid-training. You will collaborate with research scientists and engineers to develop new algorithms, build and evaluate model capabilities, and contribute to the next generation of embodied AI.

Our internships are twelve (12) to twenty-four (24) weeks long and we have various start dates throughout the year.

Responsibilities

Develop scalable mid-training methods for WAMs using large-scale egocentric video and heterogeneous robot data. Investigate representation and objective design for learning from human egocentric video. Address challenges in training on web-scale, weakly-labeled data, including embodiment and viewpoint gaps, data curation and filtering, and domain mixture weighting. Build efficient training and data pipelines, and study how downstream policy performance scales with dataset size, model capacity, and compute. Design experiments and benchmarks to measure what mid-training contributes, isolating its effect on downstream post-training and generalization across tasks and embodiments. Collaborate with research scientists and engineers to evaluate mid-trained models in simulation and on real robots. Communicate research findings and publish results at leading machine learning, computer vision, and robotics venues.

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 training of video models or robotics foundation models 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 Intent to return to degree program after the completion of the internship/co-op Experience training VLAs, WAMs, or other foundation models for embodied decision-making Experience with diffusion or flow-based policies, action-sequence prediction, 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