[This requisition is for Computer vision/NLP-heavy Multimodal roles] Meta is seeking Research Interns to join Fundamental AI Research (FAIR) working on Multimodality. The team is committed to advancing the field of Artificial Intelligence by making fundamental advances in technologies to help interact with and understand our world. We are seeking individuals passionate in areas such as Computer Vision, Natural Language Processing, Core Machine Learning, and Deep Learning. Our interns have an opportunity to make core algorithmic advances and apply their ideas at an unprecedented scale. Our teams are looking for candidates with expertise in the Computer Vision, Natural Language Processing & Multimodality domains, such as: image understanding and generation, natural language understanding and generation, multimodal large language modeling, pretraining, question answering, cross-lingual and cross-domain transfer learning. Our internships are twelve (12) to sixteen (16), or twenty-four (24) weeks long and we have various start dates throughout the year. Currently has or is in the process of obtaining a PhD degree in the field of Computer Vision, Natural Language Processing, Machine Learning, Artificial Intelligence, or relevant technical field. Research and/or work experience in Computer vision Research and/or work experience in Natural Language Processing Research and/or work experience in Machine Learning or Deep Learning Experience in Python, C++, or other related languages and with PyTorch framework. Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment Intent to return to the 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 in Computer Vision (CVPR, ECCV, ICCV, Language (ACL, EMNLP, NAACL), and/or core Machine Learning (NeurIPS, ICLR, ICML) 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