Meta was built to help people connect and share, and over the last decade our tools have played a critical part in changing how people around the world communicate with one another. With over a billion people using the service and more than fifty offices around the globe, a career at Meta offers countless ways to make an impact in a fast growing organization.We are seeking Research Interns to join Meta's Fundamental AI Research (FAIR) group. We are committed to advancing the field of artificial intelligence by making fundamental advances in technologies to help interact with and understand our world. We are seeking individuals passionate in areas such as embodied AI, robotics (navigation, manipulation), AR/VR/MR, egocentric computer vision, grounded 3D perception, simulation and sim2real transfer, pre-training for embodied agents from offline data, digital agents, planning based LLMs, multi-agent coordination, and human-AI interaction. Candidates should have background knowledge from foundational areas such as deep learning, reinforcement learning, computational statistics, and applied mathematics. Our interns have an opportunity to make core algorithmic advances and apply their ideas at an unprecedented scale.Our internships are twelve (12) to twenty-four (24) weeks long and we have various start dates throughout the year.

• Perform fundamental and applied research to push the scientific and technological frontiers of embodied artificial intelligence.
• Invent and/or improve novel data driven paradigms for embodied intelligence.
• Explore and conceptualize ways to leverage various data modalities (images, video, text, tactile, etc) and the roles they play in various levels of embodied reasoning and decision making.
• Investigate paradigms that can deliver a spectrum of embodied behaviors - from simulated characters in VR to personal assistants in MR/AR to robots in physical spaces, and from short horizon, low level to long horizon, high level.
• Enable long-horizon reasoning for Embodied AI tasks (navigation, mobile manipulation, instruction following, question answering, goal and intent prediction, egocentric activity recognition, collaboration with humans) in human environments given natural-language instructions, like "clean up the house," or "where are my glasses."
• Enable low-level skills for Embodied AI tasks (broad dexterous and functional manipulation, rigid to deformable objects in-hand to against the environment) in a generalizable manner.
• Collaborate with researchers and cross-functional partners including communicating research plans, progress, and results.
• Publish research results and contribute to research that can be applied to Meta product development.

• Currently has or is in the process of obtaining a Ph.D. degree in Artificial Intelligence, Robotics, or relevant technical field.
• Research experience in embodied AI, robotics, computer vision, machine learning, human-AI interaction, and computer science.
• Experience with Python, C++, C, or other related language.
• Experience with deep learning frameworks such as PyTorch.
• 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.
• Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or conferences Machine Learning (NeurIPS, ICML, ICLR), Robotics (ICRA, IROS, RSS, CoRL), Computer Vision (CVPR, ICCV, ECCV) and NLP (ACL, NAACL), or similar.
• Experience building systems based on machine learning and/or deep learning methods.
• Experience working and communicating cross functionally in a team environment.
• Experience in advancing AI techniques in computer vision, including core contributions to open source libraries and frameworks in computer vision.
• Publications or experience in machine learning, AI, computer vision, optimization, computer science, statistics, applied mathematics, or data science.
• Experience solving analytical problems using quantitative approaches.
• Experience setting up ML experiments and analyze their results.
• Experience manipulating and analyzing complex, large scale, high-dimensionality data from varying sources.
• Experience in utilizing theoretical and empirical research to solve problems.
• Experience with deep learning frameworks.