Engineering Manager, ML Recommendations

Etsy•New York, NY

2d•Hybrid

About The Position

We are looking for an Engineering Manager to lead the Recommendations Retrieval team at Etsy. This team owns the core retrieval algorithms that power Etsy’s recommendation surfaces, helping buyers discover unique items they’ll love across the homepage, listing pages, cart, and beyond. As we invest in next-generation retrieval capabilities, you’ll guide the team through scaling our candidate generation systems, modernizing our retrieval stack, and integrating machine learning to improve personalization and relevance at scale. You will lead a talented group of engineers and applied scientists responsible for building and operating the systems that generate high-quality, personalized candidates for Etsy’s recommendation models. This is a high-impact opportunity to shape the foundation of how Etsy connects buyers with the items and sellers they’ll love most. This is a full-time position reporting to the Director of Engineering, Recommendations. In addition to salary, you will also be eligible for an equity package, an annual performance bonus, and our competitive benefits that support you and your family as part of your total rewards package at Etsy. For this role, we are considering candidates based in the United States. Candidates living within commutable distance of Etsy’s Brooklyn Office Hub may be the first to be considered. For candidates within commutable distance, Etsy requires in-office attendance once or twice per week depending on your proximity to the office. Etsy offers different work modes to meet the variety of needs and preferences of our team. Learn more details about our work modes and workplace safety policies here.

Requirements

Bachelor’s, Master’s, or PhD in Computer Science, Engineering, or a related technical field.
Proven experience in large-scale retrieval, recommendation, or search systems, with a strong background in candidate generation, ANN/vector search, or ML-powered personalization.
2+ years of experience leading engineering teams, ideally in real-time recommendation, search, or ads systems.
Familiarity with modern retrieval and ranking techniques, including neural retrieval, two-tower models, and transformer-based embeddings and architectures.
Experience with system performance optimization, caching strategies, and scaling high-throughput, low-latency services.
Strong communication and collaboration skills; capable of bridging technical and non-technical stakeholders.
Track record of driving complex technical projects from conception to launch while maintaining a high bar for quality and reliability.
Proven people leadership in high-stakes, fast-moving environments, remaining steady and solutions-oriented through shifting priorities and ambiguous direction.

Responsibilities

Lead and grow a team of engineers and applied scientists responsible for designing, building, and maintaining candidate generation and retrieval systems at scale.
Define and execute the technical strategy for recommendations retrieval, ensuring we balance innovation, reliability, and performance.
Oversee the evolution of our retrieval infrastructure, ensuring healthy data pipelines, index quality, and system performance across recommendation surfaces.
Collaborate with cross-functional partners including Ranking, Recs Systems, ML Platform, Data Engineering, Analytics, and Product to shape the direction of the recommendations stack.
Foster a culture of technical excellence, mentorship, and continuous learning through regular design reviews, code reviews, and structured performance feedback.
Establish clear technical goals, monitor system health, and proactively identify and resolve performance and reliability bottlenecks.