AI Research Scientist, Video Generation and Post Training, FAIR

Job description

Meta is seeking a Research Scientist to join the Fundamental AI Research (FAIR) team within Meta Superintelligence Labs (MSL). Our mission is to advance the science of intelligence and develop technologies that push the boundaries of AI. We are looking for researchers with expertise in video generation and post-training of large-scale models to help build the perceptual and generative foundations for next-generation AI systems.

This role offers the opportunity to collaborate with a highly interdisciplinary team of scientists, engineers, and cross-functional partners, leveraging cutting-edge technology, resources, and research facilities.

Responsibilities

• Conduct fundamental and applied research in video generation, including generative models, video synthesis, and multimodal learning
• Develop and optimize post-training paradigms for large-scale video and multimodal models, improving their performance, robustness, and generalization
• Collaborate with teams across Meta to build perceptual foundations for real-time embodied agents and conversational AI Contribute to the development and deployment of frontier models (e.g., Llama, LMMs) and push the boundaries of video and media generation

Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience PhD or equivalent experience in Computer Science, Electrical Engineering, or a related field
• Demonstrated expertise in video generation, computer vision, or multimodal AI Experience with large-scale model training, post-training optimization techniques, and data curation Publication record in relevant fields
• Demonstrated research and software engineering experience via internships, industry or academic work experience, coding competitions, or widely used contributions in open source repositories (e.g., GitHub) Experience with video generation, video synthesis, or multimodal fusion techniques Experience with video-language models and architectures relevant to video generation and post training
• Proven track record of achieving significant results, as demonstrated by grants, fellowships, patents, or publications at leading workshops, journals, or conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV) Experience solving complex problems and evaluating alternative solutions, tradeoffs, and perspectives to determine a path forward Experience working and communicating cross-functionally in a collaborative, interdisciplinary team environment