AI Research Scientist, Language - Generative AI

Meta is seeking Research Scientists to join its Generative AI (GenAI) organization, focused on making significant advances in AI. We work on the flagship Llama language models and publish state-of-the-art research in Machine Learning. We are currently seeking talented researchers with experience in Language Research to join our EMEA sites and work with us on extending the capabilities of large foundation models. Researchers will drive impact by: (1) open sourcing high quality code and reproducible results for the community, and (2) bringing the latest research to Facebook products for connecting billions of users, (3) pushing for state-of-the-art research and publishing papers and technical reports. The chosen candidate(s) will work with a diverse and highly interdisciplinary team of scientists, engineers, and cross-functional partners, and will have access to cutting edge technology, resources, and research facilities. AI Research Scientist, Language - Generative AI Responsibilities Work towards long-term ambitious research goals, while identifying intermediate milestones. Influence progress of relevant research communities by producing publications, technical reports, and open source code. Contribute research that can be applied to Meta product development. Lead and collaborate on research projects within a globally based team. Minimum Qualifications PhD degree in Computer Science, Mathematics, or similar quantitative field. First-author publications at peer-reviewed AI conferences (e.g. *ACL, EMNLP, NeurIPS, ICML, ICLR). Experience in training, fine-tuning, and/or experimenting with foundation models beyond black-box use. Familiarity with one or more deep learning frameworks (e.g. pytorch, tensorflow, …) Ability to communicate complex research both in writing and orally for public audiences of peers. Knowledge in a programming language. Preferred Qualifications High-impact publications at peer-reviewed AI conferences (e.g. *ACL, EMNLP, NeurIPS, ICML, ICLR, ICCV,CVPR), as witnessed by citations and other signs of influencing the research community. Experience in AI research beyond completing a PhD. Experience in developing and debugging beyond ML experimentation. Experience in working in an industry research environment. Experience in coordinating the research of PhD students or other researchers.

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