Afshin Dehghani was looking for a change of pace when he stumbled upon an opportunity at DoorDash.
Category Archives: engineering
Safeguarding app health and consumer experience with metric-aware rollouts
As part of our ongoing efforts to enhance product development while safeguarding app health and the consumer experience, we are introducing metric-aware rollouts for experiments.
Beyond the Click: Elevating DoorDash’s personalized notification experience with GNN recommendation
DoorDash has redefined the way users explore local cuisine.
DoorDash expands to São Paulo with exciting Engineering opportunities
I’m thrilled to introduce our brand-new DoorDash office in São Paulo, Brazil.
Sharpening the Blur: Removing dilution to maximize experiment power
When it comes to reducing variance in experiments, the spotlight often falls on sophisticated methods like CUPED (Controlled Experiments Using Pre-Experiment Data).
Building DoorDash’s product knowledge graph with large language models
DoorDash’s retail catalog is a centralized dataset of essential product information for all products sold by new verticals merchants – merchants operating a business other than a restaurant, such as a grocery, a convenience store, or a liquor store.
Setting up Kafka multi-tenancy
Real-time event processing is a critical component of a distributed system’s scalability.
Improving ETAs with multi-task models, deep learning, and probabilistic forecasts
The DoorDash ETA team is committed to providing an accurate and reliable estimated time of arrival (ETA) as a cornerstone DoorDash consumer experience.
Introducing DoorDash’s in-house search engine
We reviewed the architecture of our global search at DoorDash in early 2022 and concluded that our rapid growth meant within three years we wouldn’t be able to scale the system efficiently, particularly as global search shifted from store-only to a hybrid item-and-store search experience.
Experiment Faster and with Less Effort
Business Policy Experiments Using Fractional Factorial Designs
At DoorDash, we constantly strive to improve our experimentation processes by addressing four key dimensions, including velocity to increase how many experiments we can conduct, toil to minimize our launch and analysis efforts, rigor to ensure a sound experimental design and robustly efficient analyses, and efficiency to reduce costs associated with our experimentation efforts.