This list contains only the countries for which job offers have been published in the selected language (e.g., in the French version, only job offers written in French are displayed, and in the English version, only those in English).
Meta is seeking a 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.
Job Responsibility:
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
Lead, plan, and execute novel research based on long-term objectives of the organization
Requirements:
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
PhD degree in the field of Artificial Intelligence, Robotics, Computer Vision, Machine Learning, Language, a related field, or equivalent practical experience
Experience with any of the following research areas: robotics, motion planning, embodied AI, human-robot interaction, sim-to-real transfer, learning from demonstration, reinforcement learning, dexterous manipulation, digital agents, vision language models, computer vision, egocentric perception, and/or LLMs
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
Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment
Nice to have:
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)
5+ 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
Experience solving complex problems and comparing alternative solutions, tradeoffs, and different perspectives to determine a path forward
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 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
Experience working and communicating cross functionally in a team environment