Learn about high impact projects that power our velocity, reliability, and innovation.
A scalable solution for supporting multiple iOS apps means leveraging a common app library and design language system.General
DoorDash's decision engine empowers customer service agents to deliver consistent, effective solutions for customer issues.Machine Learning
At DoorDash, getting forecasting right is critical to the success of our logistics-driven business, but historical data alone isn’t enough to predict future demand. We need to ensure there are enough Dashers, our name for delivery drivers, in each market for timely order delivery. And even though it seems like people’s demand for food delivery ...Culture
In September of 2019 I had one child and another on the way. At the same time, I was working as a software engineer, a career often notable for late nights and weekend work. In addition, my focus in supporting infrastructure usually requires a rotating on-call, where I might need to troubleshoot an outage outside ...Backend
Asynchronous task management using Gevent improves scalability and resource efficiency for distributed systems. However, using this tool with Kafka can be challenging. At DoorDash, many services are Python-based, including the technologies RabbitMQ and Celery, which were central to our platform’s asynchronous task-queue system. We also leverage Gevent, a coroutine-based concurrency library, to further improve the ...Data Machine Learning
Analytics teams focused on detecting meaningful business insights may overlook the need to effectively communicate those insights to their cross-functional partners who can use those recommendations to improve the business. Part of the DoorDash Analytics team’s success comes from its ability to communicate actionable insights to key stakeholders, not just identify and measure them. Many ...Backend Web
Building flexibility into the DoorDash platform lets us scale to serve a variety of retailers.Data Machine Learning
Running experiments on marketing channels involves many challenges, yet at DoorDash, we found a number of ways to optimize our marketing with rigorous testing on our digital ad platforms. While data scientists frequently run experiments, such as A/B tests, on new features, the methodology and results may not seem so clear when applied to digital ...Backend
In 2020, DoorDash engineers extracted the consumer order checkout flow out of our monolithic service and reimplemented it in a new Kotlin microservice service. This effort, part of our migration from a monolithic codebase to a microservices architecture, increases our platform’s performance, reliability, and scalability. The consumer checkout flow is one of the most critical ...Backend
DoorDash engineers built Infra Prober, a new monitoring tool, to continually look for component failures and provide accurate alerts.Data Machine Learning
DoorDash extended its machine learning platform to support ensemble models.Backend
When trying to scale a distributed system a common obstacle is not that there aren’t enough resources available, it’s that they are not being used efficiently. At DoorDash we found a similar opportunity when working to scale our point-of-sale system (POS). We were experiencing outages because our POS system could not scale to meet peak ...