About the Role

Meta is seeking a Research Scientist Intern to join the Fundamental AI Research (FAIR) Multimodal Foundations teams. The role involves developing novel computer vision algorithms, advancing AI science, and collaborating on research applicable to Meta products. This is a PhD-level internship focused on NLP, multimodal AI, and deep learning.

Key Responsibilities

• Develop novel state-of-the-art computer vision algorithms and systems using deep learning techniques
• Analyze and improve efficiency, scalability, and stability of deployed algorithms
• Perform research to advance the science and technology of intelligent machines
• Perform research enabling learning of semantics across data modalities (images, video, text, audio)
• Collaborate with researchers and cross-functional partners, communicating research plans and results

Required Skills & Qualifications

Must Have:

• Currently has or is in the process of obtaining a PhD in Computer Science, Computer Vision, AI, or related technical field
• Must obtain work authorization in the country of employment at hire and maintain it during employment
• Experience with Python, C++, C, Java, or other related languages
• Experience building systems based on machine learning and/or deep learning methods

Nice to Have:

• Intent to return to a degree program after the internship
• Proven track record with grants, fellowships, patents, or first-authored publications at leading conferences (e.g., NeurIPS, ICML, ACL)
• Experience working and communicating cross-functionally in a team environment
• Experience advancing AI techniques in computer vision, including contributions to open source libraries
• Publications or experience in machine learning, AI, computer vision, optimization, statistics, or data science
• Experience solving analytical problems using quantitative approaches
• Experience setting up ML experiments and analyzing results
• Experience manipulating and analyzing complex, large-scale, high-dimensional data
• Experience utilizing theoretical and empirical research to solve problems
• Experience with deep learning frameworks