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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
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
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
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
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
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
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
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
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
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
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
Backend

Moving e2e testing into production with multi-tenancy for increased speed and reliability

Learn how we used multi-tenancy to improve production testing standards, speed and effectiveness in this new technical blog post

Santosh Banda

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