Michelle Vu | Web Performance Engineer Detecting, preventing, and resolving performance regressions has been a standard at Pinterest for many years. Over the years, we have seen many examples showing significant business metric movements resulting from performance optimizations and regressions. These concrete examples motivate us to optimize and maintain performance. In particular, fighting regressions was made a priority because we’ve seen countless times that months of hard earned optimizations can easily be wiped out by a regression. Oftentimes, the regression was from a single line of code ..read more

Leif Sigerson | Sr. Data Scientist; Wendy Matheny | Sr. Lead Public Policy Manager; User engagement is a critical signal used by Pinterest and other online platforms to determine which content to show users. However, it is widely known that optimizing purely for user engagement can surface content that is low-quality (e.g., “clickbait”), or even harmful. Our CEO, Bill Ready, explains that if we’re not careful, content ranking can surface the “car crash we can’t look away from”. On the other hand, “if you ask somebody after they saw the crash, ‘you want to see another one?’, the vast major ..read more

Adopted by Pinterest multiple user facing surfaces, Ads, and Board. Jianjin Dong | Staff Machine Learning Engineer, Content Quality; Qinglong Zeng | Senior Engineering Manager, Content Quality; Andrey Gusev | Director, Content Quality; Yangyi Lu | Machine Learning Engineer, Home Feed; Han Sun | Staff Machine Learning Engineer, Ads Conversion Modeling; William Zhao | Software Engineer, Boards Foundation, Jay Ma | Machine Learning Engineer, Ads Lightweight Ranking LinkSage: Graph Neural Network based model for Pinterest off-site content semantic embeddingsBackground Pinterest is the visual ..read more

Monil Mukesh Sanghavi | Software Engineer, Real Time Analytics Team; Xiao Li | Software Engineer, Real Time Analytics Team; Ming-May Hu | Software Engineer, Real Time Analytics Team; Zhenxiao Luo | Software Engineer, Real Time Analytics Team; Kapil Bajaj | Manager, Real Time Analytics Team At Pinterest, one of the pillars of the observability stack provides internal engineering teams (our users) the opportunity to monitor their services using metrics data and set up alerting on it. Goku is our in-house time series database providing cost efficient and low latency storage for metrics data. Und ..read more

Yulin Lei | Senior Machine Learning Engineer; Kaili Zhang | Staff Machine Learning Engineer; Sharare Zahtabian | Machine Learning Engineer II; Randy Carlson | Machine Learning Engineer I; Qifei Shen | Senior Staff Machine Learning Engineer Introduction Pinterest strives to deliver high-quality ads and maintain a positive user experience. The platform aims to show ads that align with the user’s interests and intentions, while also providing them with inspiration and discovery. The Ads Engagement Modeling team at Pinterest plays a crucial role in delivering effective advertising campaigns a ..read more

Sam Wang | Sr. Technical Program Manager; Joe Gordon | Sr. Staff Software Engineer At Pinterest we are continuously looking for ways to improve our developer experience, and we have recently shipped AI-assisted development for everyone while balancing safety, security, and cost. In this blog post, we share our journey of unlocking AI-assisted development, from the initial idea to the General Availability (GA) stage. Join us as we delve into the opportunities, challenges, and successes we encountered along the way. Like many companies, we initially disallowed the use of Large Languag ..read more

Jeremy Krach | Staff Security Engineer, Platform Security Background Several years ago, Pinterest had a short incident due to oversights in the policy delivery engine. This engine is the technology that ensures a policy document written by a developer and checked into source control is fully delivered to the production system evaluating that policy, similar to OPAL. This incident began a multi-year journey for our team to rethink policy delivery and migrate hundreds of policies to a new distribution model. We shared details about our former policy delivery system in a conference talk from ..read more

Author: Cathy Qian, Aayush Mudgal, Yinrui Li and Jinfeng Zhuang Image from https://unsplash.com/photos/w7ZyuGYNpRQIntroduction At Pinterest, our mission is to bring everyone the inspiration to create a life they love. People often come to Pinterest when they are considering what to do or buy next. Understanding this evolving user journey while balancing across multiple objectives is crucial to bring the best experience to Pinterest users and is supported by multiple recommendation models, with each providing real-time inference with an overall latency of 200–300 milliseconds. In particula ..read more

A Journey from GBDT to Multi-Task Ensemble DNN Aayush Mudgal | Senior Machine Learning Engineer, Ads Ranking Conversion Modeling; Han Sun | Senior Machine Learning Engineer, Ads Ranking Conversion Modeling; Matt Meng | Senior Machine Learning Engineer, Ads Ranking Conversion Modeling; Runze Su | Machine Learning Engineer II, Ads Ranking Conversion Modeling; Jinfeng Zhuang | Staff Machine Learning Engineer, Ads Ranking Conversion Modeling In this blog post, we will share how we improved Pinterest’s conversion optimization performance by leveraging Deep Neural Networks (DNN), Multi-Task Lea ..read more

Rajath Prasad, Senior Engineering Manager Pinterest serves more than 480M monthly users and has grown to be a global destination for visual inspiration. As Pinterest has grown, so have our storage requirements. In 2020, anticipating the growing needs of the business and to simplify our storage offerings, we decided to consolidate our different key-value systems in the company into a single unified service called KVStore. While KVStore was the client facing abstraction, we also built a storage service called Rockstorewidecolumn: a wide column, schemaless NoSQL database built using RocksDB. This ..read more