Tilman Drerup, Moe Moazzami, Shih-Ting Lin, Greg Reda (and many more)IntroductionAt Instacart, artificial intelligence is fundamentally changing the way our machine learning engineers operate. In a prior blog post, we used one of our teams as a case study to illustrate how the emergence of agents has reshaped what machine learning engineers spend their time on. The post below goes a few levels deeper and zooms in on the machine learning modeling process itself, an area where recent developments in AI-assisted research have opened up exciting new frontiers that we are now actively exploring.…
How we built a Slack-native on-call reasoning harness at Instacart that shortens time to first insight, speeds up theory testing, and turns tribal knowledge into reusable infrastructure.Key Contributors: Karthik Halukurike, Gabe de Oliveira, Hassan Jallad, Alan WongOn-call work is a race to turn noisy signals into shared judgment. The hardest minutes of that race aren’t the ones spent fixing the bug — they’re the ones spent figuring out what the bug even is, while everyone in the thread is asking the same question from a slightly different angle.Blueberry is the system we built at Instacart…
Sergio Camelo, Caitlin Kearns, Matias Cersosimo, and Tilman DrerupAs artificial intelligence increases the velocity of engineering and science teams, experimental throughput is set to become a bottleneck for many product decisions. Many companies can now build faster than they can experiment, with queues of good ideas running the risk of not being tested because of lack of experimental capacity.This problem is particularly severe in marketplaces, where the presence of spillover and cannibalization effects between experimental units requires cluster-level randomization techniques. That…
Benjamin S. KnightScaling Marketplace experiments requires specialized statistical techniques. We examine why standard ordinary least squares regression (OLS) becomes computationally intractable when controlling for high-cardinality categories. We then dive into the underlying math and demonstrate how modern packages — specifically Fixest and Pyfixest — bypass these limitations. We conclude by benchmarking these methods to show their real-world impact on processing speed, memory efficiency, and estimator precision.At Instacart we strive to give our customers access to all the fresh foods and…
Key Contributors: Karuna Ahuja, Marko Avdalovic, Soroush Sobhkhiz, Shrikar Archak, Xiyu Wang, Ji Chao Zhang, Hao YanIntroductionEvery time a user opens Instacart, they see product recommendations: on the retailer home page, in search results, and alongside their cart. Many of these recommendations are sponsored products surfaced by a retrieval model that decides which products to show from a vast ads product catalog. A relevant ad helps users discover products they didn’t know they needed; a less relevant one generates friction.Two years ago, we introduced Contextual Recommendations (CR), a…
Key Contributors: Shrikar Archak, Karuna Ahuja, Soroush Sobhkhiz, Marko Avdalovic, Xiyu Wang, JiChao Zhang, Hao Yan, Chris HartleyIntroductionOperating a grocery catalog at Instacart’s scale means managing millions of products across thousands of categories. Every product is assigned to a category in our hierarchical taxonomy like “Dairy > Cheese > Parmesan”. These categories provide broad classification, but they miss the connections that drive how customers actually shop.For example, a customer is building a cheese board. They’ve added Parmigiano Reggiano, and now they need accompaniments.…
Levi Boxell, Tilman Drerup, Alexandr LenkThe Economics Team at Instacart is an applied science team that operates at the intersection of machine learning engineering and economics. Similar to other applied science teams, our work involves a good chunk of engineering, steeped in statistics, math, theory, and strategy. And while that is still at the heart of what we do today, the surprisingly rapid emergence of artificial intelligence has also fundamentally altered our work in ways that we did not see coming.With this post, we want to provide a brief check-in and share an analysis of the…
TL;DRBackground: Marketing Across Marketplace and StorefrontInstacart operates across two distinct commerce experiences:Instacart Marketplace, our first-party consumer marketplaceStorefront Pro, our white-label e-commerce platform for retailersFor years, our marketing automation infrastructure was built primarily to support Marketplace use cases. That model worked well in a first-party environment, where the product experience, customer relationship, and brand were all centrally managed by Instacart.Storefront Pro introduced a very different set of requirements. As the platform scaled to more…
Authors: Trey Zhong, Xiyu WangContributors: Joseph Haraldson, Sharad Gupta, Sarah LamacchiaIntroductionCarrot Ads is Instacart’s omnichannel retail media solution that allows retailer partners to build and scale their own advertising businesses on either their owned-and-operated (O&O) websites and apps or their whitelabel Storefront hosted by Instacart. Carrot Ads empowers retailers and CPG brands to accelerate revenue, while improving the customer experience, engagement and Ads return on investment. It features enterprise-grade infrastructure, AI-powered optimization, years of proprietary…
Key Contributors: Moein Hasani, Hamidreza Shahidi, Trace Levinson, Guanghua ShuIntroductionAt Instacart, we are laser-focused on improving the user experience by making shopping feel easy, engaging, and personalized. Our discovery surfaces play a central role in bringing this to life. Alongside explicit Search intents, discovery is our opportunity to meet customers’ implicit needs, presenting them with the most relevant and inspiring content we have to offer. The main discovery surface within the Instacart app, referred to here as the “Shopping Hub”, is one of the most critical in this…
Key Contributors: Youming Luo, Andrew Tanner, Matas Sriubiskis, Sylvia Lin, Sikun Zhu, Lei Li, Xiao ZhouIntroductionCaper is Instacart’s AI-powered smart cart that provides customers with a fast, seamless, and intuitive shopping experience. We achieve this through computer vision and multi-sensor fusion to power accurate product recognition and effortless checkout. Delivering this experience requires Caper’s AI models to understand what truly happens in stores — the movement, intention, and decisions unfolding across every grocery aisle.Historically, our ability to learn from production…
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