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Meta Platforms · Menlo Park, CA

AI Research Scientist, Robotics - Meta Superintelligence Labs

Menlo Park, CAResearch & Science

Listed on Meta Platforms’s official careers site. Listed as of October 18, 2026. Check that it is still open and apply there.

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Job description

Meta is seeking an AI Research Scientist to join Meta Superintelligence Labs. Individuals in this role are expected to be recognized experts in identified research areas such as artificial intelligence, machine learning, robotics, and embodied AI, particularly including areas such as transfer learning, learning from demonstration, reinforcement learning, action-conditioned world models, perception, representation learning, robot control, navigation, mobile manipulation, dexterous manipulation, and vision-language models.

You should have a keen interest in producing new, open science to make embodied agents more intelligent.

Responsibilities

Perform fundamental and applied research to push the scientific and technological frontiers of embodied artificial intelligence. Invent/improve novel data-driven paradigms for robotics, leveraging a variety of modalities (images, video, text, audio, tactile, etc). Investigate paradigms that can deliver a spectrum of embodied behaviors from simulated characters to real robots, and from short-horizon, low-level to long-horizon, high-level intelligence.

Develop algorithms based on state-of-the-art machine learning and neural network methodologies. Define, build and benchmark new capabilities needed for the next generation of AI. Conduct research towards long-term research goals while identifying intermediate milestones. Execute novel research based on long-term objectives of the organization.

Qualifications

Currently has, or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience.

Degree must be completed prior to joining Meta Currently has or is in the process of obtaining a PhD degree in the field of Artificial Intelligence, Robotics, Computer Vision, Machine Learning, Language, a related field, or equivalent practical experience Experience in relevant robotics research areas such as: robot learning, reinforcement learning, imitation learning, action-conditioned world models, task and motion planning, sim-to-real transfer, robotic control, dexterous manipulation, navigation, vision language models, or generally embodied AI Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment

  • 2+ years of industry experience in relevant robotics related research areas, such as: robot learning, reinforcement learning, imitation learning, action-conditioned world models, task and motion planning, sim-to-real transfer robotic control, manipulation, navigation, or generally embodied AI PhD degree in the field of Artificial Intelligence, Robotics, Computer Vision, Machine Learning, Language, a related field, or equivalent practical experience
  • Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as publications at leading workshops, journals or conferences in Machine Learning (NeurIPS, ICML, ICLR), Robotics (ICRA, IROS, RSS, CoRL), Computer Vision (CVPR, ICCV, ECCV)
  • Demonstrated research and software engineering experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g.

GitHub) Experience solving complex problems and comparing alternative solutions, tradeoffs, and different perspectives to determine a path forward Experience working and communicating cross functionally in a team environment Experience with robotics frameworks like ROS, along with experience working with robot simulations and real-world hardware Experience building systems based on machine learning and/or deep learning methods Experience with deep learning frameworks (such as pytorch, tensorflow) and Python Experience with manipulating and analyzing complex, large scale, high-dimensionality data from varying sources

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