Managing Search Teams: Field Stories & Practical Takeaways
Session Abstract
Even though search teams are structured differently across the industry, they share common challenges like balancing learning with delivery and nurturing a culture built for continuous iteration. This talk distills a decade of organizational lessons from building Yelp’s AI-powered search into repeatable patterns for any team facing similar hurdles.
Session Description
Search quality is never “done.” Especially in the AI-powered search world, search teams run on a cycle of research, infrastructure changes, and model refinements whose results dictate the next move. That built-in uncertainty makes managing a search team very different from managing a deterministic product-feature team. Drawing on a decade of building Yelp’s AI-powered search, this talk offers real-world lessons on four fronts:
• Team structure: Trade-offs between different org shapes (joint pods vs. split infra/relevance groups) and their impact on day-to-day operations
• Project execution: Ways to balance open-ended research projects with hard product delivery dates
• Stakeholder management: Tactics for setting expectations on long-running machine learning explorations
• Culture: Practices that develop product-minded relevance engineers
The takeaways are anchored in concrete stories and we’ll close with fresh observations on how LLM workstreams have revised some of these lessons.
Search teams may not share a single canonical shape, but the underlying patterns in this talk should translate to any organization steering the moving target of modern, AI-driven search.
