# Agentic Machine Learning Modeling at Instacart

DevFeed: [Agentic Machine Learning Modeling at Instacart](<https://devfeed.tech/articles/agentic-machine-learning-modeling-at-instacart-20102.md>)

Original publisher: [Read original article](<https://tech.instacart.com/agentic-machine-learning-modeling-at-instacart-fb3ecd295ee7?source=rss----587883b5d2ee---4>)

Author: Tilman Drerup

Published: 2026-09-03T16:05:30Z

Content type: article

Language: en

Sources: [Instacart](<https://devfeed.tech/sources/instacart.md>)

Topics: [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [Model Development](<https://devfeed.tech/topics/model-development.md>), [AI Development](<https://devfeed.tech/topics/ai-development.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Development](<https://devfeed.tech/topics/development.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [agentic](<https://devfeed.tech/tags/agentic.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [development](<https://devfeed.tech/tags/development.md>), [instacart](<https://devfeed.tech/tags/instacart.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [models](<https://devfeed.tech/tags/models.md>), [research](<https://devfeed.tech/tags/research.md>)

## AI overview

Instacart describes how its machine learning engineers are exploring AI-agent-assisted modeling loops. The approach keeps engineers responsible for defining problems and supervising work while agents develop hypotheses, implement experiments, and evaluate them.

## Source excerpt

Tilman Drerup, Moe Moazzami, Shih-Ting Lin, Greg Reda (and many more) Introduction At 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. Based on the combined insights of a small horde of MLEs, we will share some of the big wins, the disappointments, and the surprises we encountered along the way. Let's jump in. Big Picture Machine learning models permeate Instacart's marketplace, powering everything from search results to replacement recommendations and expected delivery times. Each of these models is carefully built, maintained, and iterated upon by our crafty MLEs. And while the hours spent on modeling tend to be extremely impactful for the company, the process itself is quite time-consuming and requires an MLE to make a myriad of both small and large decisions. These decisions include, among other things, the right modeling architecture, the appropriate choice for a large number of hyperparameters, the feature set to include, or the most suitable loss functions. All of these decisions are often grounded in a fairly lengthy review of the associated literature as well as a good dose of MLE intuition. Unfortunately, given the combinatorial complexity of this problem and the constraints on human time, MLEs can typically only explore a small part of the entire universe of modeling options, often leaving substantial value on the table. For a few months now, MLEs across Instacart have been exploring the development of methods and tools to tackle this constraint through AI-agent-assisted modeling loops. What follows