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Software Engineer, Recommendation Systems

Meta· Engineering
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Sunnyvale, CA, Hybrid (Multiple locations)$271k - $347kFull-timePosted 3mo ago

About the role

Meta seeks a distinguished engineer to define the future of recommendation systems at scale, guiding technical direction for large-scale ranking, retrieval, and personalization infrastructure and translating research into production systems that improve user engagement and satisfaction across Meta surfaces.

Responsibilities

  • Define and drive the multi-year technical vision for recommendation and ranking systems across Meta, influencing architecture decisions that span retrieval, candidate generation, feature engineering, and multi-objective ranking
  • Identify and solve the hardest scalability and quality challenges in large-scale recommendation pipelines, including cross-system and cross-boundary issues
  • Architect extensible, reliable ranking and personalization infrastructure that serves as a foundational platform for multiple product teams
  • Develop and apply novel machine learning techniques to recommendation problems, translating research advances into production systems
  • Define new metrics and experimentation frameworks for evaluating long-term recommendation quality, linking to business outcomes
  • Drive engineering excellence across recommendation system codebases by establishing invariants, quality standards, and AI-native development practices
  • Partner with research, product, data science, and infrastructure teams to align on strategy and deliver outcomes
  • Mentor engineers on recommendation system design and scalable ranking/retrieval
  • Lead cross-functional modernization of legacy recommendation infrastructure, including migrations
  • Serve as a go-to technical authority for leadership on recommendation systems and inform company-wide investments

Qualifications

  • Bachelor's degree in Computer Science, Computer Engineering, or equivalent practical experience
  • 12+ years of experience designing, building, and scaling production recommendation, ranking, or personalization systems
  • Experience defining technical strategy and architecture for large-scale ML systems (retrieval, candidate generation, feature engineering, multi-objective ranking)
  • Experience leading cross-organizational engineering initiatives and influencing technical direction at organizational level
  • Experience developing and applying ML models at scale from inception to production impact, including experimentation design and metric definition
  • Experience identifying and resolving systemic reliability, performance, or correctness issues across distributed ML and data infrastructure
Last updated Oct 23, 2026. Interested? Apply on the company site.

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