Engineering Manager, Machine Learning

Meta is seeking an Engineering Manager to lead machine learning engineering teams building state-of-the-art recommendation systems at scale. In this role, you will manage teams of ML engineers and technical leaders working on Meta Recommendation Systems (MRS) — from data pipelines and model development to training infrastructure and production deployment. You will shape the technical strategy for recommendation and ranking initiatives, drive SOTA model adoption within your teams, and partner closely with product, data science, and research to deliver recommendation systems that have meaningful impact across Meta's products and platforms.

Manage multiple teams of ML engineers and technical leaders delivering large-scale recommendation and ranking systems across model development, training, evaluation, and production serving Drive the technical strategy and roadmap for MRS initiatives, influencing decisions around SOTA model architectures, recommendation algorithms, and deployment approaches Actively engage with technical direction and code quality across teams, staying current with state-of-the-art research in recommendation systems and applying cutting-edge techniques Partner with product, data science, and research to define recommendation system problem formulations, prioritize ranking experiments, and translate model improvements into measurable product outcomes Recruit, develop, and retain ML engineers and engineering leaders with deep expertise in recommendation systems and ranking models Champion adoption of SOTA techniques in recommendation systems, including deep learning approaches, transformer-based models, and multi-objective optimization Proactively identify and resolve execution risks across recommendation system projects, including data quality issues, model performance regressions, training instability, and infrastructure bottlenecks Establish a team culture that values code quality, rigorous experimentation practices, and continuous learning from recommendation systems research Hold leaders accountable for performance, technical depth in ranking and personalization, and cross-functional engagement Represent the team's work and priorities to leadership, communicating recommendation system tradeoffs, SOTA advances, and strategic implications clearly