About the Role

Research Scientist Intern role focusing on developing egocentric AI systems for AR/VR and robotics. The intern will work on cutting-edge research in large foundation models, generative AI, and 3D scene understanding. This is a PhD-level internship requiring expertise in computer vision, machine learning, and foundation models.

Key Responsibilities

• Plan and execute cutting-edge research to advance machine perception, future prediction, and 4D scene understanding
• Collaborate with researchers and engineers across Meta to develop experiments and prototypes
• Help design, setup, and run practical experiments related to large-scale sensing and machine reasoning
• Contribute to the development of an egocentric AI system for contextual-AI-enabled AR devices and humanoid robots
• Tackle research challenges and innovate novel computer vision and machine learning techniques

Required Skills & Qualifications

Must Have:

• Currently has or is in the process of obtaining a PhD in computer vision, machine learning, robotics, or computer graphics
• Must obtain and maintain work authorization in the country of employment
• Knowledge and hands-on experience with 3D computer vision
• Hands-on experience implementing large foundation models and generative models (LLMs, VLMs, video diffusion models, etc.)
• Experience working within Python environments such as PyTorch
• Experience working in a Unix environment

Nice to Have:

• Ability to work a consecutive 24 weeks
• Proven track record of significant results demonstrated by grants, fellowships, patents, or first-authored publications
• Strong track-record of published research in generative modeling, large foundation models, robotics, neural reconstruction, and neural rendering
• Strong programming experience using Python and PyTorch
• Demonstrated software engineer experience via internships, work experience, or open source contributions
• Intent to return to a degree-program after the internship
• Experience working and communicating cross functionally in a team environment