Pinterest Engineering Blog - Medium · Scour

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Making User-Sequence Data More Cost-Efficient, Faster, and Easier to Use

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An Engineer’s Guide to Better AI Skills: Implementing a Testing Process to Optimize Agent…

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Enhancing Ad Relevance: Integrating Real-Time Context into Sequential Recommender Models

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Optimizing ML Workload Network Efficiency (Part I): Feature Trimmer

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From Clicks to Conversions: Architecting Shopping Conversion Candidate Generation at Pinterest

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Smarter URL Normalization at Scale: How MIQPS Powers Content Deduplication at Pinterest

Finding zombies in our systems: A real-world story of CPU bottlenecks

Discussed on Hacker News

Scaling Recommendation Systems with Request-Level Deduplication

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Performance for Everyone

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Evolution of Multi-Objective Optimization at Pinterest Home feed

Building an MCP Ecosystem at Pinterest

Unified Context-Intent Embeddings for Scalable Text-to-SQL

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Unifying Ads Engagement Modeling Across Pinterest Surfaces

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Bridging the Gap: Diagnosing Online–Offline Discrepancy in Pinterest’s L1 Conversion Models

Piqama: Pinterest Quota Management Ecosystem

Drastically Reducing Out-of-Memory Errors in Apache Spark at Pinterest

GPU-Serving Two-Tower Models for Lightweight Ads Engagement Prediction

Next Generation DB Ingestion at Pinterest

Beyond Two Towers: Re-architecting the Serving Stack for Next-Gen Ads Lightweight Ranking Models…

Discussed on Hacker News

Ads Candidate Generation using Behavioral Sequence Modeling

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