Meta is seeking a researcher to join the Fundamental AI Research (FAIR) team, a research organization focused on advancing the state-of-the-art in AI. In this role, you'll work with world-class researchers at FAIR on fundamental and exploratory research. The FAIR RAM (Reasoning, Alignment, Memory) team focuses on research in developing learning algorithms with enhanced reasoning, memory and alignment methods. Our current projects span improved learning objectives, self-supervised learning objectives, higher-level reasoning, and new memory techniques. Our organization is motivated by producing new science to understand intelligence and technology towards achieving advanced machine intelligence.
Responsibilities
Perform research to advance the science and technology of intelligent machines, particularly on topics around reasoning, alignment and memory Perform research that enables learning the semantics of data (images, video, text, audio, and other modalities) Work towards long-term research goals, while identifying immediate milestones Influence progress of relevant research communities by producing publications Open source high quality code and produce reproducible research
Qualifications
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience A PhD in AI, computer science, data science, or related technical fields 2+ years of industry or equivalent PostDoctoral experience in relevant research areas, such as: machine learning, optimization, computer vision, natural language processing First-authored publications at peer-reviewed conferences, such as ACL, EMNLP, NeurIPS, ICML, ICLR and other similar venues Experience holding an industry, postdoctoral, faculty, or government researcher position Research background in machine learning, artificial intelligence, computational statistics, applied mathematics, or related areas Research publications reflecting experience in theoretical or empirical research Experience in developing and debugging in Python or similar programming languages Experience in analyzing and collecting data from various sources Research and engineering experience demonstrated via publications, grants, fellowships, patents, internships, work experience, open source code, and / or coding competitions Experience in developing optimization algorithms and theory, distributed training of large-scale machine learning models, and comparing alternative solutions, trade-offs, and different perspectives Experience collaborating in a team environment on research projects