Research Scientist Intern, Efficient World Foundation Models (PhD)

Meta•Burlingame, CA

2d•$7,650 - $12,667

About The Position

Meta Reality Labs Research is seeking PhD students interested in efficient AI algorithms and accelerating machine-learning applications for wearable smart glasses, including Ray-Ban Meta. The team develops system-optimized algorithms for machine perception and technologies that can bring AI assistants powered by large language models to wearable systems. The internship focuses on foundational AI technologies supporting AI-assistant and embodied-AI functionality, including world foundation models, multitasking, multimodal understanding (such as audio, visual, and spatial), and generative models. Internships last 12 to 24 weeks, with various start dates throughout the year.

Requirements

Currently pursuing or in the process of obtaining a PhD in Computer Science, Electrical Engineering, or a related field.
Must obtain work authorization in the country of employment by the time of hire and maintain it throughout employment.
At least 2 years of research experience in developing machine-learning or computer-vision models, optimizing edge-computing algorithms, or distributed-compute architectures.
At least 2 years of programming experience in Python and/or C++.
Experience with deep-learning frameworks such as PyTorch or TensorFlow.

Nice To Haves

Experience customizing multimodal large language models.
Experience with world foundation models, self-supervised learning, and embodied AI.
Experience with generative models and high-quality visual generation.
Experience deploying machine-learning algorithms for real-time execution on mobile and distributed platforms.
Experience with neural architecture search or developing and deploying algorithms under hardware constraints.
A record of significant results demonstrated through first- or co-authored publications at leading workshops or conferences (for example, CVPR, ICCV, TinyML, NeurIPS, or ICML), patents, or grants.
Intent to return to the degree program after completing the internship.

Responsibilities

Research the design, modeling, and execution of efficient machine-learning algorithms.
Develop and optimize novel machine-learning, computational-imaging, and generative-AI algorithms for edge applications.
Research the development and optimization of edge and distributed-computing algorithms.
Collaborate with and support researchers across disciplines.
Communicate research agendas, progress, and results.
Prototype, build, and characterize experimental systems with real hardware.