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.
Minimum 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)
Preferred Qualifications
• 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
