ML Engineering Manager — Recommender Systems at Scale

Meta; Bellevue, Menlo Park (WA, CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Meta is seeking an Engineering Manager to lead machine learning engineering teams building state-of-the-art recommendation systems at scale. You will manage ML engineers and leaders across data pipelines, model development, training infrastructure, and production deployment.

You will shape technical strategy for recommendation and ranking initiatives, drive SOTA model adoption, and partner with product, data science, and research to deliver impactful systems across Meta's products.

Qualifications

• 8+ years in software engineering with ML systems focus.
• 4+ years leading engineering teams including leaders.
• Experience shaping ML strategy across data ingestion to production serving.
• Proven cross-functional partnership with product, data science, and research.
• Experience recruiting and building high-performing ML teams.
• Hands-on ML model development with PyTorch or TensorFlow.
• Experience with large-scale recommendation or ranking systems.
• Commitment to responsible, ethical AI practices.

Responsibilities

• Lead ML engineering teams building state-of-the-art recommendation systems at scale.
• Shape technical strategy and roadmaps for ML systems across the full model lifecycle.
• Partner with product, data science, and research to deliver impactful systems.
• Drive adoption of SOTA research into production.

Skills

ML leadership

Cross-functional collaboration

ML model development

Production ML systems

Technical strategy

Team management

Recruiting and talent development

Research-to-production integration

Tools

PyTorch

TensorFlow

ML infrastructure

Job description

Meta is seeking an Engineering Manager to lead machine learning engineering teams building state-of-the-art recommendation systems at scale. You will manage ML engineers and leaders across data pipelines, model development, training infrastructure, and production deployment.

You will shape technical strategy for recommendation and ranking initiatives, drive SOTA model adoption, and partner with product, data science, and research to deliver impactful systems across Meta's products.