Meta is seeking a distinguished engineer to help define the future of recommendation systems that serve billions of people across Facebook, Instagram, Reels, Marketplace, and other Meta surfaces. In this role, you will set the technical direction for large-scale ranking, retrieval, and personalization infrastructure, identifying opportunities to rethink foundational systems from the ground up using AI-native approaches. You will operate at the intersection of machine learning research and production engineering, driving step-change improvements in how Meta surfaces the most relevant content, connections, and experiences to every person on our platforms.
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 those that cross multiple systems or fall at abstraction boundaries Architect extensible, reliable ranking and personalization infrastructure that serves as a foundational platform for multiple product teams and engineering organizations Develop and apply novel machine learning techniques to recommendation problems, translating research advances into production systems that deliver measurable improvements in user engagement and satisfaction Define new metrics and experimentation frameworks for evaluating long-term recommendation quality, connecting them to organization-level priorities and business outcomes Drive engineering excellence across recommendation system codebases by establishing invariants, quality standards, and AI-native development practices that prevent whole classes of reliability and correctness issues Partner with research, product, data science, and infrastructure teams to align on technical strategy, navigate complex trade-offs, and deliver outcomes that advance Meta's competitive position in personalization Mentor engineers across the organization on recommendation system design, debugging complex ranking and retrieval issues, and building systems that scale to billions of users Lead cross-functional initiatives to modernize legacy recommendation infrastructure, including migration projects in complex and mature technical environments Serve as a go-to technical authority for leadership on recommendation systems, producing strategic communications and conceptual frameworks that inform company-wide investment decisions
Qualifications
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, 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 machine learning systems, including retrieval, candidate generation, feature engineering, and multi-objective ranking Experience leading cross-organizational engineering initiatives, including driving consensus across multiple teams and influencing technical direction at the organizational level Experience developing and applying machine learning models at scale, from inception through production impact, including experimentation design and metric definition Experience identifying and resolving systemic reliability, performance, or correctness issues across distributed machine learning and data infrastructure Experience rearchitecting or rebuilding recommendation or ranking systems using AI-native approaches that delivered order-of-magnitude improvements in quality or efficiency Proficiency with deep learning frameworks such as PyTorch or TensorFlow applied to large-scale embedding models, two-tower architectures, or sequential recommendation models Track record of industry-recognized contributions to recommendation systems, information retrieval, or personalization through publications, patents, or open-source work Experience building real-time feature serving, approximate nearest neighbor retrieval, or low-latency inference infrastructure for recommendation at internet scale