DoorDash Engineering Blog

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Making an Impact: Starting My Engineering Career at DoorDash

iOS engineer Dhruv Upadhyay describes his experience joining DoorDash as a recent Computer Science grad.

Backend Culture Data General Machine Learning Mobile Web

2020 Hindsight: Building Reliability and Innovating at DoorDash

DoorDash recaps a number of its engineering highlights from 2020, including its microservices architecture, data platform, and new frontend development.

Mobile Web

Implementing Theming in DoorDash’s Design Language System

Adding the concept of Theming to DoorDash's design language system made it easier for engineers to use standardized design elements in all of our products.

Lindsey Menges
Machine Learning

Things Not Strings: Understanding Search Intent with Better Recall

For every growing company using an out-of-the-box search solution there comes a point when the corpus and query volume get so big that developing a system to understand user search intent is needed to consistently show relevant results.  We ran into a similar problem at DoorDash where, after we set up a basic “out-of-the-box” search ...

Siddharth Kumar Jimmy Zhou Xiaochang Miao Ashwin Kachhara
Machine Learning

Iterating Real-time Assignment Algorithms Through Experimentation

DoorDash operates a large, active on-demand logistics system facilitating food deliveries in over 4,000 cities. When customers place an order through DoorDash, they can expect it to be delivered within an hour. Our platform determines the Dasher, our term for a delivery driver, most suited to the task and offers the assignment. Complex real-time logistic ...

Sifeng Lin Longsheng Sun

The Undervalued Skills Candidates Need to Succeed in Data Science Interviews

After interviewing over a thousand candidates for Data Science roles at DoorDash and only hiring a very small fraction, I have come to realize that any interview process is far from perfect, but there are often strategies to improve one’s chances . Over the course of our interviews, I’ve come across some great candidates who ...

Lokesh Bisht

Future-proofing: How DoorDash Transitioned from a Code Monolith to a Microservice Architecture

In 2019, DoorDash’s engineering organization initiated a process to completely reengineer the platform on which our delivery logistics business is based. This article represents the first in a series on the DoorDash Engineering Blog recounting how we approached this process and the challenges we faced. In traditional web application development, engineers write code, compile it, ...

Cesare Celozzi

Minimizing Risk for API Extraction in a Major Migration Project

DoorDash engineering describes its three step process for safely migrating business logic as APIs.

David Chen
Data Machine Learning

Building a Gigascale ML Feature Store with Redis, Binary Serialization, String Hashing, and Compression

When a company with millions of consumers such as DoorDash builds machine learning (ML) models, the amount of feature data can grow to billions of records with millions actively retrieved during model inference under low latency constraints. These challenges warrant a deeper look into selection and design of a feature store — the system responsible ...

Arbaz Khan Zohaib Sibte Hassan
Data Machine Learning

Uncovering Online Delivery Menu Best Practices with Machine Learning

Learn how we analyzed over 100K online delivery menus to develop menu best practices

Finn Qiao

Hello Seattle: DoorDash Expands its Engineering Footprint to the Pacific Northwest

DoorDash opens a new tech office in Seattle to support its Drive and DashMart business lines.

David Azose

Hot Swapping Production Tables for Safe Database Backfills

DoorDash engineering explains how to edit large data tables safely and quickly in a production database.

Justin Lee

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