Senior Engineering Manager, Search

Instacart Instacart · Consumer · United States · Remote · Leadership (Engineering)

Instacart is seeking a Senior Engineering Manager to lead their dedicated Search engineering team. This role will own the strategy and execution for Single-Retailer Search (SRS), Cross-Retailer Search (XRS), search suggestions, typeahead, and whitelabel search. The team is heavily ML-weighted and operates one of Instacart's largest revenue surfaces, focusing on applied search and ranking. The manager will lead and grow a senior team of engineers and MLEs, responsible for relevance, ranking, and infrastructure. Key responsibilities include owning the technical strategy, roadmap, and goals; driving growth while maintaining quality; championing LLMs in search; designing next-generation retrieval architecture; improving experimentation velocity through evaluation and tooling; and partnering with various teams. The role requires strong engineering management experience, direct experience with search/recommendations/applied ML systems, familiarity with modern retrieval techniques, and technical depth in ML and distributed systems. Experience with LLMs in production and e-commerce/ads-adjacent ranking problems is preferred.

What you'd actually do

  1. Own the team's technical strategy, roadmap, and goals across Single and Cross retailer search, query understanding, suggestions, Ads on Search.
  2. Drive top-line growth while holding a strict quality bar.
  3. Champion the use of LLMs in search.
  4. 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.
  5. Double the team's experimentation velocity by investing in evaluation, AI-native debugging tooling, and ML foundations.

Skills

Required

  • 4+ years of engineering management experience leading high-performing teams
  • 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 have

  • 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

What the JD emphasized

  • strictly
  • strict quality bar
  • next-generation search retrieval architecture
  • AI-native debugging tooling
  • ML foundations
  • modern retrieval techniques
  • large-scale distributed/backend systems
  • complex, cross-functional technical programs
  • high bar for quality and reliability
  • product and business judgment
  • low-latency, high-throughput services
  • strict SLAs
  • 0→1 or ambiguous product area

Other signals

  • Search is the single most important way customers find and add products to their cart
  • Cross-Retailer Search is how we help people decide not just _what_ to buy, but _where_ to shop
  • This is a heavily ML-weighted team operating one of Instacart's largest revenue surfaces
  • one of the most exciting frontiers in applied search and ranking
  • Champion the use of LLMs in search
  • Double the team's experimentation velocity by investing in evaluation, AI-native debugging tooling, and ML foundations