Presented By O’Reilly and Intel AI
Put AI to Work
April 29-30, 2018: Training
April 30-May 2, 2018: Tutorials & Conference
New York, NY

How DoorDash leverages AI in its world-class on-demand logistics engine

Raghav Ramesh (DoorDash)
4:50pm–5:30pm Wednesday, May 2, 2018
Implementing AI, Models and Methods
Location: Grand Ballroom East
Average rating: *****
(5.00, 1 rating)

Who is this presentation for?

  • Data scientists, machine learning engineers, software engineers, and product managers

Prerequisite knowledge

  • A basic understanding of machine learning

What you'll learn

  • Explore useful AI techniques in the on-demand logistics industry
  • Learn how DoorDash leverages ML techniques for core operations research problems


Recent years have seen a surge in marketplace companies where the common algorithmic challenge is efficient matching between two sides of the marketplace. For DoorDash, which focuses on food delivery, this problem is even more difficult because of its three-sided marketplace, where the company must identify the optimal Dasher to fulfill a delivery from a restaurant and bring it to a consumer. Raghav Ramesh highlights AI techniques used by DoorDash to enhance efficiency and quality in its marketplace and provides a framework for how AI can augment core operations research problems like the vehicle routing problem (VRP).

VRP in its simplest form is NP hard, and the real-time, quick-turnaround nature of DoorDash introduces additional challenges: delivery requests come in continuously, Dashers constantly are in movement, and the effects of variance in restaurant operations and real-world events (traffic, weather, etc.) have pronounced effects on the solutions. Thus, finding global optimality in real time becomes further intractable. DoorDash leverages various AI techniques to intelligently model the decision space and achieve near optimal solutions in seconds. Ultimately, across DoorDash’s tens of millions of deliveries, these techniques have led to shorter delivery times for consumers, higher pay for Dashers, increased income for merchant partners, and a better experience for all sides of the marketplace.

Topics include:

  • An overview of DoorDash’s AI techniques
  • Predicting time points in the life of a delivery: How predictions are made and, more importantly, how they fit in with the larger logistics engine
  • ML techniques for batching multiple deliveries together: Algorithms used in the logistics engine to decide when and how deliveries can be batched together
Photo of Raghav Ramesh

Raghav Ramesh


Raghav Ramesh is a machine learning engineer at DoorDash working on its core logistics engine, where he focuses on core AI problems: vehicle routing, Dasher assignments, delivery time predictions, demand forecasting, and pricing. Previously, Raghav worked on various data products at Twitter, including recommendation systems, trends ranking, and growth analytics. He holds an MS from Stanford University, where he focused on artificial intelligence and operations research.