Research Scientist Intern, Efficient World Foundation Models (PhD)

Meta; Burlingame (CA); On-site; USD 48,000 - 69,000; Full time

Job summary

Meta Reality Labs Research is offering an internship focusing on efficient AI algorithms and acceleration of AI ML applications for wearable smart glasses. The 12- to 24-week program includes research on foundational AI technologies such as world foundation models, multimodal understanding, and generative models.

Collaborations across disciplines and hands-on prototype work with real hardware are emphasized.

Qualifications

• 2+ years research experience in ML/vision
• Experience with edge computing algorithms
• Experience with real-time ML on mobile/distributed platforms

Responsibilities

• Research design/model/exec of efficient ML algorithms
• Develop and optimize ML and generative AI algorithms for edge applications
• Collaborate with researchers across disciplines
• Communicate research progress and results
• Prototype and characterize experimental systems with real hardware

Skills

Python programming

C++ programming

Deep learning

Multimodal LLM

Education

PhD in CS or EE or related

Tools

PyTorch

TensorFlow

Job description

About

Meta’s mission is to give people the power to build community and bring the world closer together. Through our family of apps and services, we're building a different kind of company that connects billions of people around the world, gives them ways to share what matters most to them, and helps bring people closer together. Whether we're creating new products or helping a small business expand its reach, people at Meta are builders at heart. Our global teams are constantly iterating, solving problems, and working together to empower people around the world to build community and connect in meaningful ways. Together, we can help people build stronger communities — we're just getting started.Meta Reality Labs Research is looking for upcoming scientists and researchers with great interest in efficient AI algorithms and acceleration of AI machine learning applications for wearable smart glasses [Rayban Meta]. Our team specializes in developing state-of-the-art system optimized algorithms for machine perception. With the vision of always-on AI assistance, our team is focusing on cutting edge systems that can facilitate and bring AI assistants powered by LLMs to wearables systems. This internship will focus on aspects of foundational AI technologies around world foundation models, multitasking, multimodal understanding (audio, visual, spatial, etc), Generative models, or others that support AI assistant and embodied AI functionalities.Our internships are twelve (12) to twenty-four (24) weeks long and we have various start dates throughout the year.

Responsibilities

• Research on design / model / execution of efficient ML algorithms.
• Develop and optimize novel ML, computational imaging, and generative AI algorithms for edge applications.
• Research on development and optimization of edge and distributed computing algorithms.
• Collaboration with and support of other researchers across various disciplines.
• Communication of research agenda, progress and results.
• Prototyping, building and characterizing experimental systems with real hardware.

Minimum Qualifications

• Currently has, or is in the process of obtaining a PhD in the fields of Computer Science, Electrical Engineering, or related field
• Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment
• 2+ years research experience in one or more of the following: developing machine learning and computer vision models, optimization of edge computing algorithms, or distributed compute architectures
• 2+ years experience programming in Python/C++
• Experience with Deep Learning frameworks (Pytorch, TensorFlow, etc) Experience with customizing Multi-Modal LLM
• Experience with world foundation models, self-supervised learning, and embodied AI
• Experience with generative models and high-quality visual generation
• Experience in deploying ML algorithms for real-time execution on mobile and distributed platforms
• Experience with NAS or developing and deploying algorithms with hardware constraints
• Proven track record of achieving significant results, as demonstrated by first- and/or co-authored publications at leading workshops or conferences (e.g., CVPR, ICCV, TinyML, NeurIPS, ICML), patents, or grants
• Intent to return to the degree program after the completion of the internship