AI Research Scientist - MSL FAIR Alignment

Meta is seeking AI Researchers to join the FAIR RAM (Reasoning, Alignment, Memory) team, part of the FAIR pillar in Meta Superintelligence Labs. Our team pursues curiosity-driven research to develop learning algorithms with enhanced reasoning, memory, and alignment. Current projects span long-horizon reinforcement learning + agents, improved learning + self-supervised learning objectives, higher-level reasoning, new memory techniques, and scalable alignment methods including self-alignment, self-improvement, and co-improvement. Our team is a research-centric team working across both pure research and research-to-product (R2P). We conduct cutting-edge fundamental AI research with industry-scale resources, and we openly publish our results.

Responsibilities

Define and drive research agendas in reasoning, memory, alignment, and agentic learning. Design and run large-scale experiments to test novel algorithmic ideas, including long-horizon RL, self-supervised objectives, and tool-using agents. Develop new methods for scalable alignment - self-alignment, self-improvement, and co-improvement loops that reduce dependence on human-annotated supervision. Invent and evaluate new memory architectures and techniques for models that reason over long contexts and extended horizons. Conduct cutting-edge fundamental AI research, publishing at top-tier venues, and contribute to the research community through reviewing, talks, and collaboration. Partner closely with other fundamental research teams in MSL, and research-to-product teams in other pillars.

Qualifications

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience PhD in Computer Science or a related technical field 2+ years of industry research experience in Generative AI and LLM 1+ year of experience as a formal technical lead, leading major technical initiatives with cross-functional impact, and/or influencing strategy across multiple teams Research experience in LLM post-training and/or agents Published research in leading peer-reviewed conferences (e.g., ACL, EMNLP, NeurIPS, ICML, ICLR) and/or demonstrated significant industry influence in the field of AI Proficiency in Python and a modern deep learning framework (e.g., PyTorch) Domain expertise in LLMs for agents, higher-level reasoning, alignment methodologies, and/or new memory techniques First-author publications at top peer-reviewed conferences (e.g., ACL, EMNLP, NeurIPS, ICML, ICLR) Experience working on frontier-quality, state-of-the-art Large Language Models