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Machine Learning

Retraining Machine Learning Models in the Wake of COVID-19

When the COVID-19 pandemic significantly changed how people took their meal, DoorDash had to retrain demand prediction machine learning models.

Read More Austin Cai
General Web

Things to Keep in Mind When Integrating a Map Feature to a Web App

Lessons for developing a fast, flexible, and scalable map feature on web

Read More Ying-Chun Wang
Data Machine Learning

Supporting Rapid Product Iteration with an Experimentation Analysis Platform

DoorDash engineers built Curie, a new experimentation analysis platform, to better gauge the success of product experiments.

Read More Arun Balasubramani
Backend

Eliminating Task Processing Outages by Replacing RabbitMQ with Apache Kafka Without Downtime

Learn how we utilized a custom Kafka solution to reduce outages and enable horizontal scalability for task processing

Read More Saba Khalilnaji
Machine Learning

Using a Human-in-the-Loop to Overcome the Cold Start Problem in Menu Item Tagging

Learn how we built a classification model quickly, cheaply, and at scale

Read More Abhi Ramachandran
General

Four Challenges When Launching a Product Partnership

From a product engineering perspective, external partnerships can be tricky. Here are four best practices to follow.

Read More Manori Thakur
Mobile

A Framework For Speedy and Scalable Development Of Android UI Tests

Learn how a Fluent design pattern can help create easy to read, scaleable, automated UI tests for Android development

Read More Nishant Soni
Backend

Building Reliable Workflows: Cadence as a Fallback for Event-Driven Processing

Reengineering our event-driven delivery service for DoorDash Drive into Kotlin, we added the open source Cadence as a fallback for retries.

Read More Alan Lin
General

Scaling DoorDash’s Geospatial Innovation with a Location-Based Delivery Simulator

Learn how we built a discrete event simulator for location data tests

Read More Janice Lee
Mobile

Avoiding Conditional Navigation Pitfalls When Implementing the Android Navigation Library

Learn how we we were able to utilize the Android Navigation library without sacrificing user experience

Read More Maria Sharkina
Machine Learning

Optimizing DoorDash’s Marketing Spend with Machine Learning

Learn how we utilized cost curves to automate our marketing campaigns at scale

Read More Aman Dhesi
Backend General

Scaling Splunk Securely by Building a Custom Terraform Provider

Ensure your growing team can search, analyze, and visualize data securely by integrating Splunk with a custom built Terraform provider.

Read More Esha Mallya

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