At DoorDash, users commonly conduct searches using precise queries that compound multiple requirements.
Category Archives: AI & ML
Unleashing the power of large language models at DoorDash for a seamless shopping adventure
Photo: Courtesy of The AI Conference
Imagine a bustling marketplace where consumers seamlessly connect with local merchants to get everything from groceries to gifts delivered to their doorsteps.
Part 1: DoorDash 2024 summer intern projects
DoorDash provides an engaging internship program where software engineering interns are deeply integrated into our Engineering teams, allowing them to gain practical, real-world experience that complements their academic learning.
Precision in Motion: Deep learning for smarter ETA predictions
In the fast-paced world of food delivery, accurate estimated time of arrival, or ETA, predictions are not just a convenience; they’re a critical component of operational efficiency and customer satisfaction.
Path to high-quality LLM-based Dasher support automation
The independent contractors who do deliveries through DoorDash – “Dashers” – pick up orders from merchants and deliver them to customers.
How DoorDash is pushing experimentation boundaries with interleaving designs
We’ve traditionally relied on A/B testing at DoorDash to guide our decisions.
Beyond the Click: Elevating DoorDash’s personalized notification experience with GNN recommendation
DoorDash has redefined the way users explore local cuisine.
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.
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.
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.