Research Scientist Intern, Efficient World Foundation Models (PhD) at Meta

Redmond, Washington, United States -

Full Time

Start Date

Immediate

Expiry Date

25 Apr, 26

Salary

12134.0

Posted On

25 Jan, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Computer Vision, Edge Computing, Distributed Computing, Python, C++, Deep Learning, Generative Models, Multimodal Understanding, AI Algorithms, Prototyping, Optimization, Research, Collaboration, Experimental Systems, Self-Supervised Learning, Real-Time Execution

Industry

Software Development

Description

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) Preferred Qualifications 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 $7,313/month to $12,134/month + benefits

Responsibilities

The intern will conduct research on the design and execution of efficient machine learning algorithms and develop novel algorithms for edge applications. They will also collaborate with other researchers and communicate their research progress and results.