Engineering Manager, Machine Learning - Meta

Polluxa, Inc.; Menlo Park (CA); On-site; USD 190,000 - 230,000; Full time

Job summary

Polluxa, Inc. is seeking an Engineering Manager for Machine Learning to oversee multiple ML engineering teams responsible for building and operating large-scale recommendation and ranking systems.

You will drive strategy, architecture, and deployment practices, while mentoring engineers and aligning research with product outcomes. You will collaborate with product, data science, and research to shape problem formulations, prioritize experiments, and translate improvements into measurable product

Qualifications

• 8+ years of software engineering with focus on ML systems
• 4+ years managing engineering teams
• Experience driving technical strategy for ML systems across full model lifecycle
• Experience partnering cross-functionally with product, data science, and research teams
• Experience recruiting and retaining ML engineering talent in high-impact areas

Responsibilities

• Manage multiple teams of ML engineers and technical leaders delivering large-scale recommendation and ranking systems from model development to production serving
• Drive the technical strategy and roadmap for ML systems initiatives across architectures and deployment approaches
• Engage with technical direction and code quality, staying current with state-of-the-art research in recommendation systems
• Partner with product, data science, and research to define problem formulations and translate model improvements into measurable outcomes
• Recruit, develop, and retain ML engineers and engineering leaders with expertise in recommendation systems
• Champion adoption of SOTA techniques including deep learning, transformer models, and multi-objective optimization
• Identify and resolve execution risks across projects, including data quality issues, model performance regressions, and infrastructure bottlenecks
• Establish a team culture valuing code quality, experimentation discipline, and continual learning from research
• Hold leaders accountable for performance and cross-functional engagement
• Represent the team’s work to leadership with clear tradeoffs and strategic implications

Job description

Engineering Manager, Machine Learning 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

Minimum Qualifications:

• 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

Preferred Qualifications:

• 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