Staff Research Scientist - ML Innovation for Products

ML Innovation for Products team in the ML modeling organization is seeking a staff Research Scientist to join forces in driving innovation in retrieval, ranking, and auction systems across Meta's core Ads business. Our mission is to advance recommendation systems through applied ML, RL, and LLM research, delivering measurable impact on Meta's revenue growth.

Our applied machine learning and research team within Meta's applied machine learning organization is focused on the state-of-the-art 0-to-1 ML that powers retrieval, ranking, and auction across Meta's ads systems. You will join a multi-disciplinary team working on advanced machine learning techniques, including reinforcement learning, graph/representation learning, sequence modeling, and long-term value optimization. Our projects span memory-augmented modules for LLMs (memory modules for LLMs), graph learning with multimodal representations, and RL-driven dynamic tuning of ad ranking models. We collaborate closely with other orgs and

Responsibilities

Research and develop innovative ML/AI models, including memory-based sequence modeling, LLMs and reinforcement learning or graph learning
Design methods, tools, and infrastructure to analyze and leverage rich multimodal and sequential data sets
Drive and demonstrate measurable business impact contributing directly to Meta's revenue growth
Set technical direction for projects, collaborating with and mentoring researchers and engineers
Transition and deliver research work into production, impacting Meta's Ads ranking ecosystem
Collaborate with cross-functional teams to drive business impact through ML innovation

Qualifications

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
PhD or MSc degree in Computer Science, Engineering, Electrical Engineering, Statistics, Mathematics, or related field
Experience developing machine learning models at scale, from inception to business impact
Programming experience in Python and hands-on experience with frameworks such as PyTorch
Exposure to architectural patterns of large-scale software applications
Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
Demonstrated experience and self-driven motivation in solving analytical problems using quantitative approaches Experience with reinforcement learning, graph neural networks, or large language models (LLMs)
Experience with recommendation systems, retrieval, or ranking at scale
Track record of publications in top ML/AI venues (NeurIPS, ICML, ICLR, KDD, etc.)
Track record of setting technical direction for complex projects
Experience mentoring researchers or engineers
Demonstrated ability to translate research into production systems with measurable business impact
Experience in ads, auction systems, or monetization ML