DoorDash Engineering Blog

General

DoorDash’s May 12th Outage

Learn more about what triggered the DoorDash May12th outage

Ryan Sokol 2 Minute Read

Backend

3 Changes to Expand DoorDash’s Product Search Beyond Delivery

Focusing on delivery allowed DoorDash to build a food search engine, but expanding beyond food with more SKUs and merchants requires a substantial upgrade.

Sonic Wang

Xiaochang Miao

Yongshuang Wang 21 Minute Read

Backend Data

How We Applied Client-Side Caching to Improve Feature Store Performance by 70%

Learn about which caching libraries we considered, the analysis of our system and how we were able to use experiments to validate our approach.

Kornel Csernai 18 Minute Read

Backend

Using Fault Injection Testing to Improve DoorDash Reliability

When failure is inevitable, building fault tolerance with fault injection testing ensures that failures do not bring the platform down with them

Christopher Meiklejohn 13 Minute Read

Machine Learning

Building the Model Behind DoorDash’s Expansive Merchant Selection

Having a quality selection did not happen by accident. Learn about the ML models that power the diverse, high quality selection on our platform

Lu Wang

Ying Yang

Chen Dong 10 Minute Read

Culture General

6 questions with DoorDash’s New VP of Engineering, Liangxiao Zhu

We’re thrilled to welcome Liangxiao, our first VP of Engineering, to DoorDash!

DoorDash 5 Minute Read

Data

3 Principles for Building an ML Platform That Will Sustain Hypergrowth

Learn about the principles and big bets that enabled to scale and maintain our ML platform which supports our data users and data scientists

Hien Luu 16 Minute Read

Machine Learning

Using Gamma Distribution to Improve Long-Tail Event Predictions

Which loss function is best for long tail event prediction? learn how we used gamma distributions to boost eta prediction accuracy

Pratik Parekh

Zhe Jia 8 Minute Read

Web

Building a Common Web Library for Fast Implementations of Risk Frictions

To quickly implement new fraud prevention "frictions" we built a common library with React.js Typescript and Apollo graph. Read the guide

Sri Venkatesh Sankaran 10 Minute Read

Web

Improving Web Page Performance at DoorDash Through Server-Side Rendering with Next.JS

Displaying enticing product images with fast load speeds on high-traffic pages can be a hard. Learn how we implemented server side rendering with next.js

Patrick El-Hage

Duncan Meech 16 Minute Read

Machine Learning

Using a Multi-Armed Bandit with Thompson Sampling to Identify Responsive Dashers

Epsilon-Greedy, The Upper Confidence Bound, Thompson Sampling: which is the best Multi-armed bandit algorithm for promotion optimization?

Arjun Sharma 10 Minute Read

Machine Learning

Ship to Production, Darkly: Moving Fast, Staying Safe with ML Deployments

Learn how DoorDash balanced ML models' release speed and reliability by shipping darkly in order to manage fraud model deployments

Bob Nugman 8 Minute Read

Welcome to the DoorDash Engineering Blog

Newest Article

Featured Article

DoorDash’s May 12th Outage

3 Changes to Expand DoorDash’s Product Search Beyond Delivery

How We Applied Client-Side Caching to Improve Feature Store Performance by 70%

Using Fault Injection Testing to Improve DoorDash Reliability

Building the Model Behind DoorDash’s Expansive Merchant Selection

6 questions with DoorDash’s New VP of Engineering, Liangxiao Zhu

3 Principles for Building an ML Platform That Will Sustain Hypergrowth

Using Gamma Distribution to Improve Long-Tail Event Predictions

Building a Common Web Library for Fast Implementations of Risk Frictions

Improving Web Page Performance at DoorDash Through Server-Side Rendering with Next.JS

Using a Multi-Armed Bandit with Thompson Sampling to Identify Responsive Dashers

Ship to Production, Darkly: Moving Fast, Staying Safe with ML Deployments

Welcome to the DoorDash Engineering Blog

Newest Article

Featured Article

DoorDash’s May 12th Outage

3 Changes to Expand DoorDash’s Product Search Beyond Delivery

How We Applied Client-Side Caching to Improve Feature Store Performance by 70%

Using Fault Injection Testing to Improve DoorDash Reliability ￼

Building the Model Behind DoorDash’s Expansive Merchant Selection

6 questions with DoorDash’s New VP of Engineering, Liangxiao Zhu

3 Principles for Building an ML Platform That Will Sustain Hypergrowth￼

Using Gamma Distribution to Improve Long-Tail Event Predictions

Building a Common Web Library for Fast Implementations of Risk Frictions

Improving Web Page Performance at DoorDash Through Server-Side Rendering with Next.JS

Using a Multi-Armed Bandit with Thompson Sampling to Identify Responsive Dashers

Ship to Production, Darkly: Moving Fast, Staying Safe with ML Deployments

Using Fault Injection Testing to Improve DoorDash Reliability

3 Principles for Building an ML Platform That Will Sustain Hypergrowth