Research Scientist Intern – Robotics, World Models & Embodied AI (PhD)

Meta; Greater London; Hybride; GBP 20 000 - 31 000; Plein temps

Résumé du poste

Meta Reality Labs Research seeks Research Scientist Interns to advance AI, robotics, and AR/VR through world models, 3D generation, and tactile-enabled perception. Interns collaborate with experts to push the boundaries of embodied AI using data from real robots and egocentric devices.

Qualifications

• Pursuing or holding a PhD in Computer Vision, Robotics, ML, AI, CS, or a related STEM field.
• Experience with deep learning frameworks and Python for research.

Responsabilités

• Help build machine perception, 3D generative systems, and control tech for robotic agents and AI systems.
• Contribute to robot foundation models such as WAMs and VLAs.
• Collaborate with researchers and engineers on multiple research streams and competitions.

Connaissances

Python

PyTorch

Deep Learning

Research Communication

Cross-cultural Collaboration

Robotics

English Proficiency

Formation

PhD in Computer Vision, Robotics, ML, AI, CS, or related STEM field

Outils

MuJoCo

Isaac Sim

Isaac Lab

Description du poste

Meta Reality Labs Research (RL Research) brings together a world-class R&D team of researchers and engineers with the shared goal of developing AI, Robotics, and AR/VR technology across the spectrum. The Surreal Spatial AI group is seeking Research Scientist Interns to help build the machine perception, 3D generative systems, and control technology that allow robotic agents and AI systems to perceive, understand, reason about, and interact with the world around them.

Our research puts multi-modal generative world models at the core of machine perception and robotic control, using data from egocentric devices such as Project Aria alongside data from physical robots. As an intern, you will work with our research scientists and engineers on robot foundation models, including world action models (WAMs) and vision-language-action models (VLAs). Projects can span several research streams, including exploring novel architectures, grounding these models in 3D, learning action and goal representations, and integrating new sensor modalities such as touch.

Interns in this role are proficient in research areas such as action-conditioned world models, 3D computer vision, representation learning, learning from human video and demonstrations, cross-embodiment transfer, tactile sensing, reinforcement learning, and dexterous manipulation. You should have a keen interest in producing new, open science that makes embodied agents more intelligent.

Our internships are twelve (12) to twenty-four (24) weeks long and we have various start dates throughout the year.

• Currently has, or is in the process of obtaining, a PhD degree in Computer Vision, Robotics, Machine Learning, Artificial Intelligence, Computer Science, or a related STEM field
• Research experience in robotics, 3D computer vision, deep learning, such as multimodal or 3D generative modeling, predictive world models, scene understanding, egocentric perception, learning from demonstration, reinforcement learning, or learning-based robotic control
• Experience with deep learning frameworks (e.g., PyTorch, TensorFlow) and Python
• Experience communicating research to public audiences of peers
• Interpersonal experience: cross-group and cross-culture collaboration
• Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment
• Intent to return to the degree program after the completion of the internship/co-op
• Experience working with physics simulation platforms (e.g., Isaac Sim, Isaac Lab, MuJoCo)
• Hands-on experience with real robot hardware, such as collecting data on, debugging, or deploying learned policies to physical robots, beyond simulation alone
• Experience training or evaluating large-scale robot foundation models (e.g., VLAs, WAMs), world models, or generative video models
• Experience applying 3D computer vision to embodied AI, such as 3D scene understanding, reconstruction, or 3D-aware policies and world models
• Experience with action or goal representation learning, such as latent action models or goal-conditioned policy learning
• Experience integrating tactile, force/torque, or other non-visual sensing into robot learning
• Experience with reinforcement learning, long-horizon planning, kinematics, or sim-to-real transfer
• Demonstrated research and software engineering experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g., GitHub)
• Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, or publications at leading conferences or journals such as CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, CoRL, RSS, ICRA, or IROS
• Ability to commit to 24 consecutive weeks