Pinterest Engineering @PinterestEng
We're the engineers behind Pinterest, building a visual discovery engine powered by the latest in machine learning, billions of ideas and 600+ million users. engineering.pinterest.com San Francisco + beyond Joined September 2014-
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Manas serves billions of embeddings across 80+ clusters at Pinterest, but scaling to tens of billions, we needed new approaches: quantization (50%+ memory savings), SSD-based serving (10x less RAM), & multi-embedding retrieval for richer ranking. Details: medium.com/pinterest-engi…
Get to know the story behind building our new multimodal AI foundation with NVIDIA. Our team shares insights about enabling Pinterest Assistant to run 25x more visual context per request. medium.com/pinterest-engi…
Today, @Pinterest announced a new foundation for multimodal AI built with @nvidia to support a growing range of products that rely on both images and language. Learn more: newsroom.pinterest.com/news/newsroom-…
@vibellingham @Pinterest We're happy we could help out. Let us know if you have any further questions or concerns.
@vibellingham @Pinterest Hi there, Shuffles is a collage tool that allows you to create, publish and share visual content. For now, it's only available on iOS. To access Shuffles, you need to have an iOS device that has been updated to the latest version. You can download Shuffles from the App Store: ...
@ArpdStudio @Pinterest Hi there, Thanks for the feedback! We've shared it with our team and we'll keep it in mind as we create new features and make improvements to existing ones. Have a great day!
In our latest blog, we introduce Conditional Learned Retrieval, a powerful two-tower architecture combining state-of-the-art ML and modern RecSys methods to balance user engagement with contextual relevance to users' various interests. Learn how we did it: medium.com/pinterest-engi…
🚀We break down RPP - our purpose-built terraform execution engine for securing tens of thousands of AWS resources across hundreds of workspaces. 🔒See how we combine OIDC role-chaining, backend checks, and PR dual controls to lock down multi-repo IaC 👇 medium.com/pinterest-engi…
@SEvilloon146 Hi there! We're here to help! Send us a DM and we'll check it out. Thanks!
@SEvilloon146 @Pinterest Hi there! We're here to help! Send us a DM and we'll check it out. Thanks!
Calling all aspiring product managers, designers and researchers 📢 Applications for Pinterest’s 2026 Products Apprenticeship Program are now open. Apply now: pinterestcareers.com/departments/ap…
Schema changes shouldn't break your offline DB ingestion pipeline. Our latest blog explores how Pinterest automates schema evolution for its next-generation DB ingestion framework, safely propagating schema changes across Kafka, Flink, Spark, and Iceberg. medium.com/pinterest-engi…
Adding a second GPU node made training 5x slower. Our team shares how we moved foundation model training from a broken 0.2x baseline to 7.5x scaling by fixing networking, profiling, communication, and distributed topology. medium.com/pinterest-engi…
Our next Pinterest Labs Talk in one week away! 🚨 UC Berkeley's Dawn Song will join us for a discussion on recent advancements in AI & LLM agents and the risks they present. Register now to attend: pages.beamery.com/Pinterest/page…
@fa_r1_ha @Pinterest Hi there! We're here to help! Send us a DM and we'll check it out. Thanks!
Join us June 25 for our next Pinterest Labs Talk with UC Berkeley's Dawn Song for a discussion on AI & LLM agent advancements and key risks, moderated by Chuck Rosenberg. Register now 👉 pages.beamery.com/Pinterest/page…
Real-time ad retrieval power-up 🚀 Hybrid inference combines sequential transformers + context to blend high-intent signals with long-term user history. Result: 3x–10x Recall@K, ~300% higher median candidate relevance, and measurable ROAS lift. medium.com/pinterest-engi…
New on the Blog: For large-scale ML systems, moving less data can be just as important as computing faster. This post shares how we reduce unnecessary feature-transfer overhead to improve network efficiency in production. Read more: medium.com/pinterest-engi…
From Determinantal Point Processes to sliding spectrum decomposition, here's how our team evolved multi-objective optimization at Pinterest to balance what people engage with and what keeps the experience inspiring. medium.com/pinterest-engi…
This automatically measures User Perceived Latency on over 60 surfaces, saving two engineer-weeks per feature and encouraging platform-wide performance optimization. Learn more: medium.com/pinterest-engi…
At Pinterest, we solved the expensive, manual setup of Android performance metrics by integrating Visually Complete logic into the BaseSurface class. Our "All-In-One Solution" story 🧵👇
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