[Remote] Research Scientist Intern, AI Research Multi-modal Post-Training (PhD)

Note: The job is a remote job and is open to candidates in USA. Meta is a technology company that helps people connect and share, and they are seeking Research Scientist Interns in the Meta Superintelligence org. The role involves conducting research in generative AI and collaborating with cross-functional teams to make algorithmic advances that can be applied to Meta's products.

Responsibilities

• Perform research to advance the science and technology of generative AI
• Perform research that enables learning the semantics of data at scale (images, video, text, audio, and other modalities)
• Improve and propose new methods for post-training foundation models across the spectrum of techniques including reinforcement learning and supervised fine tuning
• Collaborate with researchers and cross-functional partners including communicating research plans, progress, and results
• Devise better data-driven models of image multi-modal understanding
• Publish research results and contribute to research that can be applied to Meta product development

Skills

• Currently has or is in the process of obtaining a Ph.D. degree in Computer Science, NLP, Reinforcement Learning (RL), Computer Vision, Artificial Intelligence, or relevant technical field
• Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment
• Experience in Python or other related programming languages
• Experience building systems based on machine learning and/or deep learning methods
• Intent to return to a degree-program after the completion of the internship/co-op
• Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as publications at leading workshops or conferences such as NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, and ACL
• Experience with Generative AI, including diffusion models and VLMs
• Background in advancing AI techniques in at the intersection of foundation model training and RL including core contributions to open source libraries and frameworks
• Publications or experience in machine learning, AI, computer vision, RL, NLP, optimization, computer science, statistics, applied mathematics, or data science
• Experience solving analytical problems using quantitative approaches
• Experience setting up ML experiments and analyzing their results
• Experience manipulating and analyzing complex, large scale, high-dimensionality data from varying sources
• Experience in utilizing theoretical and empirical research to solve problems
• Experience working and communicating cross functionally in a team environment

Benefits

• Benefits

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