The independent contractors who do deliveries through DoorDash — “Dashers” — pick up orders from merchants and deliver them to customers.
Category Archives: AI & ML
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.
Personalizing the DoorDash Retail Store Page Experience
The DoorDash retail shopping experience mission seeks to combine the best parts of in-person shopping with the power of personalization.
Transforming MLOps at DoorDash with Machine Learning Workbench
It is amusing for a human being to write an article about artificial intelligence in a time when AI systems, powered by machine learning (ML), are generating their own blog posts.
How DoorDash Improves Holiday Predictions via Cascade ML Approach
At DoorDash, we generate supply and demand forecasts to proactively plan operations such as acquiring the right number of Dashers (delivery drivers) and adding extra pay when we anticipate low supply.
How DoorDash Built an Ensemble Learning Model for Time Series Forecasting
In real-world forecasting applications, it is a challenge to balance accuracy and speed.