In a marketplace, risks inevitably converge on two key underlying questions: Who is this, and how confident are we that this action is legitimate?
Category Archives: ML Infrastructure
Building Ask DoorDash (Part 6): A Grocery Reorder Case Study
When a consumer asks Ask DoorDash to “reorder my usuals,” the request sounds simple.
Triton Auto Tuner: Finding the Right Triton Configuration for Hundreds of Models
How a reusable benchmark fleet made Triton configuration measurable across hundreds of production and shadow models, with mean QPS lift rising from 1.75x at 4 vCPU to 6.78x at 32 vCPU.
Building Ask DoorDash (Part 5): A Grounded Interface For Shopping Agents
This post covers the client and platform boundaries behind user experience and interactions with Ask DoorDash: how we moved from hackathon carousels to that model, how one artifact serves three readers, and how context and state stay consistent as consumers move through DoorDash.
Building Food Metadata with LLM Juries, Context Optimization & Multimodal AI
DoorDash serves a vast and diverse set of merchants, with every restaurant, menu, and dish expressed in its own unique way.
Inside DoorDash’s one-click simulation and evaluation platform for support chatbots
Shipping high-quality support chatbots is an end-to-end problem.
Lessons learned building DoorDash’s clusterless ML feature store
There has never been a technology revolution bigger than the one we are experiencing right now.
How DoorDash built an AI code reviewer engineers actually listen to
Many engineering orgs have tried bolting AI reviewers onto their pull requests (PRs).
Beyond Single Agents: How DoorDash is building a collaborative AI ecosystem
Knowledge at DoorDash is vast and distributed, spread across experimentation platforms, metrics hubs, dashboards, wikis, and the institutional wisdom embedded in team chats.
Building an anomaly detection platform at DoorDash to catch fraud trends early
Fraud doesn’t always kick the door down.