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Machine Learning

Machine Learning

Things Not Strings: Understanding Search Intent with Better Recall

For every growing company using an out-of-the-box search solution there comes a point when the corpus and query volume get so big that developing a system to understand user search intent is needed to consistently show relevant results.  We ran into a similar problem at DoorDash where, after we set up a basic “out-of-the-box” search ...

Siddharth Kumar Jimmy Zhou Xiaochang Miao Ashwin Kachhara Machine Learning

Iterating Real-time Assignment Algorithms Through Experimentation

DoorDash operates a large, active on-demand logistics system facilitating food deliveries in over 4,000 cities. When customers place an order through DoorDash, they can expect it to be delivered within an hour. Our platform determines the Dasher, our term for a delivery driver, most suited to the task and offers the assignment. Complex real-time logistic ...

Sifeng Lin Longsheng Sun Data Machine Learning

Building a Gigascale ML Feature Store with Redis, Binary Serialization, String Hashing, and Compression

When a company with millions of consumers such as DoorDash builds machine learning (ML) models, the amount of feature data can grow to billions of records with millions actively retrieved during model inference under low latency constraints. These challenges warrant a deeper look into selection and design of a feature store — the system responsible ...

Arbaz Khan Zohaib Sibte Hassan Data Machine Learning

Uncovering Online Delivery Menu Best Practices with Machine Learning

Learn how we analyzed over 100K online delivery menus to develop menu best practices

Finn Qiao Machine Learning

Solving for Unobserved Data in a Regression Model Using a Simple Data Adjustment

Learn how our team optimized prep time estimates while overcoming censored data

JH Sri Santhosh Hari Data Machine Learning

Improving Online Experiment Capacity by 4X with Parallelization and Increased Sensitivity

To speed up the development of new features we needed a way to increase our experiment capacity. Learn how we improved it by 4X

Jessica Zhang Yixin Tang Data Machine Learning

How DoorDash is Scaling its Data Platform to Delight Customers and Meet our Growing Demand

Learn the challenges and best practices to successfully growing a data platform organization

Sudhir Tonse Machine Learning

Leveraging Causal Modeling to Get More Value from Flat Experiment Results

Using Heterogeneous Treatment Effects to improve personalization

Mitchell Koch Jared Bauman 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.

Austin Cai 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.

Arun Balasubramani 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

Abhi Ramachandran Machine Learning

Optimizing DoorDash’s Marketing Spend with Machine Learning

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

Aman Dhesi

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