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

Meta; Menlo Park (CA); On-site; USD 55,000 - 83,000; Part time

Job summary

Meta Reality Labs Research is seeking a Research Scientist Intern to develop scalable mid-training methods for world action models (WAMs) and vision-language-action models (VLAs) for robotic manipulation. You will collaborate with researchers and engineers to advance embodied AI on large-scale egocentric video and robot data.

Internships span 12 to 24 weeks with multiple start dates; you will design experiments, build data pipelines, and publish results at top venues in ML, vision, and robotics.

Qualifications

• Pursuing or has a PhD in CS/ML/AI/Robotics.
• Experience training video models or robotics foundation models.
• Experience with Python and PyTorch.
• Experience designing controlled experiments and debugging ML algorithms.
• Experience with VLAs, WAMs or embodied decision-making is preferred.
• Experience with distributed training across GPUs/nodes.
• Proven research track record (papers/grants/patents).

Responsibilities

• Develop scalable mid-training methods for WAMs using large-scale data.
• Investigate representation and objectives for learning from human egocentric video.
• Address challenges in training on web-scale data, including embodiment and domain gaps.
• Build efficient training and data pipelines and study scaling effects.
• Design experiments and benchmarks to measure mid-training contributions.
• Collaborate with researchers and engineers to evaluate models in simulation and on real robots.
• Publish research findings at leading ML, CV, and robotics venues.

Skills

Python

PyTorch

Experiment design

Distributed training

Robotics experience

Research publications

Education

Ph.D. in Computer Science/ML/AI/Robotics

Job description

About

Meta Reality Labs Research is a team of researchers and engineers pushing the frontier of AI and robotics. Our team is building the next generation of world action models (WAMs) and vision-language-action models (VLAs) for robotic manipulation. We are seeking a Research Scientist Intern to develop scalable methods for mid-training world action models on large-scale egocentric video and robot data.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.

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 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