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How DoorDash Secures Data Transfer Between Cloud and On-Premise Data Centers

Learn how DoorDash built a secure data transfer to a new payment processing vendor by establishing a private network link using AWS Direct Connect.

Roger Zeng

Leveraging CockroachDB’s Change Feed for Real-Time Inventory Data Processing

In this post, we explore how DashMart’s engineering team used CockroachDB’s changefeed to enable real time inventory updates

Irene Chen Aleks Pesti

Balancing Velocity and Confidence in Experimentation

Figuring out how to balance our experimentation speed with the necessary controls to maintain trust is never easy. Learn DoorDash's approach.

Stas Sajin

Adapted Switch-back Testing to Quantify Incrementality for App Marketplace Search Ads

Learn how DoorDash developed a switchback testing method to perform incrementality testing on an app marketplace ad platform

Kanhua Pan Yingying Chen

How to Boost Code Coverage with Functional Testing

Introducing a non manual functional testing approach that can be run like unit tests locally or in a Continuous Integration (CI) pipeline.

Lev Neiman Venkataramanan Kuppuswamy Carlos Herrera James Lamine

2022 DoorDash Summer Intern Projects Article #2

Learn about more of our 2022 summer intern projects in this second article

Taige Zhang Shuyang Wu Zhiyang Zhang Andy Jiang Kenny Chi
Machine Learning

Augmenting Fuzzy Matching with Human Review to Maximize Precision and Recall

When we encountered a business problem that required an error rate of zero, we turned to a classification model with human review

Robert B. Kaspar Mitchell Koch

DoorDash 2022 Summer Intern Projects

Learn more about the 2022 summer intern projects in this survey post,

Wenfei Tang Jayeon Koo Jianna Liu Michael Yu Xiaochuan Xu
Machine Learning

Homepage Recommendation with Exploitation and Exploration

Learn how our model balances exploitation and exploration during ranking to optimize the consumer experience while simultaneously improving fairness for merchants

Yu Zhang
Data Machine Learning

Five Common Data Quality Gotchas in Machine Learning and How to Detect Them Quickly

Data preparation, represents The vast majority of work in developing machine learning models, learn how to make things easier

Kornel Csernai Devjit Chakravarti

How DoorDash Ensures Velocity and Reliability through Policy Automation

Learn how DoorDash enables their engineers to self-serve infrastructure through policy automation while ensuring reliability and speed

Lin Du Juvenal Santos

Adopting SwiftUI with a Bottom-Up Approach to Minimize Risk

Learn how DoorDash expanded the VIPER architecture to accelerate its engineers working on the iOS Dasher App

Terry Latanville Md Al Mamun

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