Research Scientist Intern, FAIR - Physical Engineering AI (PhD)

Meta•Seattle, WA

21h•$7,650 - $12,667

About The Position

Meta’s Fundamental AI Research (FAIR) lab is seeking a Research Scientist Intern to advance foundational research in physical engineering AI. The work aims to generate advanced engineering designs, including robotic hardware, vehicles, and novel semiconductors. The internship involves substantial software research engineering, large-scale data manipulation, and simulator integration. Interns can make core algorithmic advances and apply their ideas at scale. The team is particularly interested in candidates whose expertise spans physical engineering design and simulation, reinforcement learning, and LLM post-training, including post-training methods using reinforcement learning and 3D data, large-scale simulation integration, 3D computer vision, representation learning, self-supervised learning, and modeling across engineering domains. Internships last 12 to 24 weeks, with various start dates throughout the year. Interns develop and evaluate research prototypes, conduct experiments, analyze results, prepare reports and publications, present research outcomes, and contribute research that can inform Meta product development. The role requires current PhD enrollment or progress toward a PhD in a relevant technical field, or equivalent practical experience. Interns must obtain and maintain work authorization in the country of employment.

Requirements

Currently has or is pursuing a PhD in Artificial Intelligence, Physical AI, Computer Vision (3D), Machine Learning, Computer Science, Computer Engineering, Mechanical Engineering, Electrical Engineering, or a relevant technical field, or has equivalent practical experience.
Research and/or work experience in at least one of these areas: physical AI, simulation, reinforcement learning, representation learning, self-supervised learning, multimodal learning, robotics policy development, computer vision (3D), egocentric perception, embodied AI, LLMs, control theory, or optimization algorithms.
Experience with C/C++, Python, and deep learning frameworks such as PyTorch or TensorFlow.
Must obtain work authorization in the country of employment by the time of hire and maintain work authorization during employment.

Nice To Haves

Experience integrating and debugging prototype or scientific software-hardware systems, including simulation specific to mechanical, aerospace, or civil engineering.
A record of significant results demonstrated through grants, fellowships, patents, or first-authored publications at leading workshops or conferences, such as NeurIPS, ICML, ICLR, AAAI, JMLR, CVPR, ICCV, ECCV, or TPAMI.
Prior research or work experience in mechanical, aerospace, civil, or other engineering domains.
Experience advancing AI, including contributions to open-source libraries and frameworks.
Experience manipulating and analyzing complex, large-scale, high-dimensional data from varying sources.
Experience applying theoretical and empirical research to solve problems.
Experience working and communicating cross-functionally in a team environment.
Intent to return to a degree program after completing the internship or co-op.

Responsibilities

Explore and develop novel LLM post-training paradigms using reinforcement learning.
Explore and develop LLM post-training recipes using 3D data.
Integrate large-scale simulation into LLM post-training.
Investigate how to enable LLMs to solve key problems in mechanical, aerospace, civil, and other engineering disciplines.
Brainstorm with research mentors and review literature and existing solutions to challenging real-world research problems.
Develop novel solutions, implement prototypes, and conduct extensive experiments using meaningful benchmarks and metrics.
Analyze experimental results and verify conclusions.
Draft and polish research reports and/or publications.
Present research outcomes to internal and/or external audiences.
Contribute research that can be applied to Meta product development.