Engineering Manager, Machine Learning

Meta is seeking an Engineering Manager to lead machine learning engineering teams building state-of-the-art recommendation systems  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.

• 8+ years of experience in software engineering with a focus on machine learning systems, including model development, training pipelines, or ML infrastructure
• 4+ years of experience managing engineering teams, including experience managing other engineering leaders
• Experience driving technical strategy and roadmap decisions for ML systems across the full model lifecycle, from data ingestion through production serving
• Experience partnering cross-functionally with product, data science, and research teams to define ML problem scope and deliver measurable outcomes
• Experience recruiting, developing, and retaining ML engineering talent and building high-performing teams in ambiguous, high-impact areas
• Hands-on background in ML model development using frameworks such as PyTorch or Tensor Flow, with specific experience in recommendation models
• Experience managing teams working on large-scale recommendation, ranking, or retrieval systems in a production environment
• Experience with large-scale personalization systems, user modeling, and multi-objective ranking optimization
• Track record of building recommendation systems that directly influenced product metrics at significant scale
• Track record of implementing SOTA research papers into production recommendation and ranking systems
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Demonstrated ability to evaluate and adopt emerging techniques from top ML conferences (Rec Sys, KDD, NeurIPS, ICML) into production systems
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Experience with state-of-the-