Engineering Manager, Machine Learning

Meta•Menlo Park, CA

21h•$219,000 - $301,000

About The Position

Meta is seeking an Engineering Manager to lead machine learning engineering teams building large-scale recommendation systems. The role manages ML engineers and technical leaders working across Meta Recommendation Systems (MRS), including data pipelines, model development, training infrastructure, evaluation, and production deployment. The manager shapes technical strategy for recommendation and ranking initiatives, drives adoption of state-of-the-art techniques, and partners with product, data science, and research teams to deliver measurable outcomes across Meta products and platforms. The position includes guiding technical direction and code quality across teams, developing and retaining engineering talent, managing execution risks, and establishing a culture of rigorous experimentation and continuous learning. The manager also communicates team priorities, technical tradeoffs, and strategic implications to leadership.

Requirements

8+ years of software engineering experience focused on machine learning systems, including model development, training pipelines, or ML infrastructure.
4+ years of experience managing engineering teams, including managing other engineering leaders.
Experience driving technical strategy and roadmap decisions for ML systems across the model lifecycle, from data ingestion through production serving.
Experience partnering 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

Track record of building recommendation systems that directly influenced product metrics at significant scale.
Experience with state-of-the-art recommendation architectures, including deep learning, transformer-based models, and neural collaborative filtering.
Experience with large-scale personalization systems, user modeling, and multi-objective ranking optimization.
Ability to integrate AI tools to optimize or redesign workflows and drive measurable impact, such as efficiency gains or quality improvements.
Ability to evaluate and adopt emerging techniques from ML conferences such as RecSys, KDD, NeurIPS, and ICML in production systems.
Experience implementing responsible and ethical AI practices, such as risk assessment, bias mitigation, and quality and accuracy reviews.
Track record of implementing state-of-the-art research papers in production recommendation and ranking systems.
Ongoing AI skill development, such as prompt/context engineering or agent orchestration, and staying current with emerging AI technologies.
Experience managing teams working on large-scale recommendation, ranking, or retrieval systems in a production environment.
Hands-on ML model development using frameworks such as PyTorch or TensorFlow, with specific experience in recommendation models.

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 technical strategy and roadmap decisions for MRS initiatives, including model architectures, recommendation algorithms, and deployment approaches.
Guide technical direction and code quality across teams, stay current with recommendation systems research, and apply advanced techniques.
Partner with product, data science, and research teams to define recommendation problems, prioritize ranking experiments, and translate model improvements into measurable product outcomes.
Recruit, develop, and retain ML engineers and engineering leaders with recommendation systems and ranking expertise.
Champion state-of-the-art recommendation techniques, including deep learning, transformer-based models, and multi-objective optimization.
Identify and resolve execution risks, including data quality issues, model performance regressions, training instability, and infrastructure bottlenecks.
Establish a team culture that values code quality, rigorous experimentation, 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, advances, and strategic implications.