Senior Engineering Manager, Search

Responsibilities

• Own the team's technical strategy, roadmap, and goals across Single and Cross retailer search, query understanding, suggestions, Ads on Search.
• Drive top-line growth while holding a strict quality bar.
• Champion the use of LLMs in search.
• Partner with the platform team to design and ship the next-generation search retrieval architecture, while managing the latency and reliability tradeoffs of a tier-1 surface.
• Double the team's experimentation velocity by investing in evaluation, AI-native debugging tooling, and ML foundations.
• Partner closely with ML, Product, Data Science, the Home and Discovery Platform teams, the Ads organization, and whitelabel retailer teams to translate ranking changes into business and retailer outcomes.
• Hire, develop, and coach engineers, and build an inclusive, high-trust team culture.
• Roll up sleeves on technical and architectural decisions as needed.

Requirements

• 4+ years of engineering management experience leading high-performing teams, with minimum 10+ years of total engineering experience.
• Direct experience building or leading search, recommendations, ranking, or other applied ML systems, as a manager or as a senior individual contributor.
• Familiarity with modern retrieval techniques, including neural/semantic retrieval, ANN, and LLM-based query understanding or re-ranking.
• Strong technical depth across machine learning and large-scale distributed/backend systems; able to set direction with and earn the trust of staff-level engineers.
• A track record of driving complex, cross-functional technical programs from strategy through launch while holding a high bar for quality and reliability.
• Demonstrated product and business judgment: able to connect modeling and ranking decisions to metrics like conversion and revenue, and to reason through tradeoffs (e.g., relevance vs. latency, organic vs. ads).
• Strong communication and stakeholder skills; comfortable bridging technical and non-technical partners.

Nice-to-Haves

• Experience with ranking and personalization at scale: multi-stage rankers, multi-task deep models, embeddings, and real-time/session-based features.
• Experience operating low-latency, high-throughput services with strict SLAs, including on-call and reliability ownership.
• Experience leading a 0→1 or ambiguous product area, and driving alignment across teams where ownership boundaries are still forming.
• Experience working with LLMs in production, and with experimentation/A-B testing infrastructure.
• Exposure to two-sided marketplaces, e-commerce, or ads-adjacent ranking problems.