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: Engineering
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.
Inside Vera, DoorDash’s Data Agent
The DoorDash Data team is responsible for generating accurate data insights and answers to key business questions:
Why did the order volume dip in California last week?
How has a new product been pacing since launch?
Which merchants are at risk of churning from our marketing products?
ENGINEERING TEAM OVERVIEW
At DoorDash, we have many Engineering teams that support our Engineering domains.
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.
Automating Feature-Flag Cleanup at Scale with a Multi-Agent LLM System
DoorDash’s experimentation platform manages over 60,000 feature flags across roughly 623 repositories.
Delegating Engineering Work To Cloud-Based Agents
Flux is DoorDash’s cloud-based agents platform for engineers.
How DoorDash Built a Centralized Gateway for AI Agent-Tool Access
AI agents become useful when they can take action in real systems.
Building Ask DoorDash (Part 4): A Platform for Building and Evolving Agents
Introduction
We built Ask DoorDash on a common platform that lets domain teams build and evolve their agents without rebuilding the systems beneath them.