Engineering Manager, Machine Learning

Meta•Menlo Park, CA

6d•$219,000 - $301,000

About The Position

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.

Requirements

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

Nice To Haves

Hands-on background in ML model development using frameworks such as PyTorch or TensorFlow, 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 (RecSys, 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-art recommendation system architectures including deep learning, transformer-based models, and neural collaborative filtering

Responsibilities

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