Evolving Pinterest’s Embedding Retrieval Platform - 惯性聚合

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Stories by Pinterest Engineering on Medium

Beyond Two Towers: Launching the 3-Tower Engagement Co-Train Model (Part 2) Building Pinterest’s VLM Serving Stack on NVIDIA Dynamo Becoming an AI Team Scaling Conditional Learned Retrieval for Pinterest Home Feed Pinner Progression: Better Use-Case Representation Driving Weekly Active User Growth at Pinterest Securing Infrastructure at Scale: Introducing Pinterest’s Resource Provisioner Pipeline (RPP) Achieving Near-Linear Training Scalability for Pinterest’s Foundation Models Automated Schema Evolution in Pinterest’s Next-Generation DB Ingestion Framework Making User-Sequence Data More Cost-Efficient, Faster, and Easier to Use An Engineer’s Guide to Better AI Skills: Implementing a Testing Process to Optimize Agent… Enhancing Ad Relevance: Integrating Real-Time Context into Sequential Recommender Models Optimizing ML Workload Network Efficiency (Part I): Feature Trimmer From Clicks to Conversions: Architecting Shopping Conversion Candidate Generation at Pinterest Smarter URL Normalization at Scale: How MIQPS Powers Content Deduplication at Pinterest Finding zombies in our systems: A real-world story of CPU bottlenecks Scaling Recommendation Systems with Request-Level Deduplication Performance for Everyone Evolution of Multi-Objective Optimization at Pinterest Home feed

Evolving Pinterest’s Embedding Retrieval Platform

Pinterest Engineering · 2026-09-11 · via Stories by Pinterest Engineering on Medium

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