AI Research Scientist; Technical Leadership

Meta's Monetization RankingAI Research team advances state-of-the-art AI technologies to optimize retrieval, ranking, bidding, and whole page optimization for Meta ads—driving significant annual revenue across Facebook and Instagram. We are seeking a distinguished AI Research Scientist at the technical leadership level to lead foundational and applied research for Ads AI systems. In this role, you will define the long-term research vision for Ads Ranking models, spanning areas such as generative retrieval, generative ranking, LLM-Rec Sys, large-scale model architectures, and ML systems co-design.

You will shape research programs that influence Meta's advertising ecosystem and the broader AI research community, translating breakthrough findings into scalable, real-world impact.

• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• 12+ years of experience in AI research, spanning both foundational research and applied machine learning in an industry, academic, or government research setting
• Demonstrated track record of leading AI research programs of significant scope, including defining research agendas and delivering results that advance the field
• Extensive publication and citation history in peer-reviewed AI venues such as NeurIPS, ICML, ICLR, CVPR, ACL, or equivalent
• Experience developing and deploying large-scale AI or machine learning systems, including deep expertise in areas such as large language models, generative models, multimodal learning, or AI reasoning
• Experience communicating complex AI research strategy and technical decisions to diverse audiences, including research peers, engineering leaders, and executive stakeholders
• Experience building and mentoring high-performing research teams, including developing researchers into cross-functional technical leaders
• Experience with ads ranking, recommendation systems, or large-scale retrieval systems
• Experience with generative models, LLMs for recommendation, reinforcement learning, or causal inference in production settings
• Track record of industry-recognized contributions that have measurably influenced the direction of the AI research community
• PhD in Computer Science, Machine Learning, Statistics, Applied Mathematics, or a related technical field