Meta is seeking a researcher to join Meta Fundamental AI Research (FAIR), a research organization focused on making significant progress in AI. 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, perception, representation learning, robot control, navigation, mobile manipulation, dexterous manipulation, vision-language models, and contextually aware AI. The ideal candidate should have a keen interest in producing new, open science to make embodied agents more intelligent.Some research directions of interest are: - Investigate algorithmic development and application of foundation models for embodied AI and/or robotics- Representation learning for Embodied AI tasks- Developing the next generation of simulation platforms for embodied AI research (e.g. AI Habitat)- Sim-to-real transfer- Planning and learning for dynamic in-hand manipulation tasks- Performing broad dexterous and functional manipulation (rigid to deformable objects; in-hand to against the environment) in a generalizable manner- Tactile and multimodal perception- Continual learning in interactive environments- Long-horizon reasoning for Embodied AI tasks in human environments given natural-language instructions, like “clean up the house”, or “where are my glasses”- Developing efficient models deployable to robots and hardware

MINIMUM 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. Degree must be completed prior to joining Meta
• 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, LLMs, NLP, and/or tool usage
• Experience with deep learning frameworks (such as pytorch, tensorflow) and Python
• Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment

PREFERRED QUALIFICATIONS:

• 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 with manipulating and analyzing complex, large scale, high-dimensionality data from varying sources
• Experience building systems based on machine learning and/or deep learning methods
• 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

• Perform fundamental and applied research to push the scientific and technological frontiers of embodied artificial intelligence.
• Invent/improve novel data-driven paradigms for embodied intelligence, including AR, VR, and 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 ambitious research goals while identifying intermediate milestones.