You are viewing content from a past/completed QCon

Presentation: Time Predictions in Uber Eats

Track: Machine Learning for Developers

Location: Soho Complex, 7th fl.

Duration: 5:25pm - 6:15pm

Day of week: Monday

Share this on:

What You’ll Learn

  1. Hear about UberEats’ use of machine learning, what challenges they encounter and how they are solving them.
  2. Listen how UberEats is dealing with time predictions through ML.


Uber Eats has been one of the fastest-growing meal delivery services since its initial launch in Toronto in December 2015. Currently, it’s available in over 40 countries and 400 cities. The ability to accurately predict delivery times is paramount to customer satisfaction and retention. Additionally, estimates are important on the supply side as they inform when to dispatch couriers.  

This talk will cover how Uber Eats has leveraged machine learning to address these challenges. We’ll briefly talk about the implementation of the intelligent dispatch system, and compare the versions before and after introducing time predictions powered by machine learning. Then we’ll use food preparation time prediction as an example to show you how ML is applied in our engineering work step by step. In the end, we’ll quickly go over the time predictions of estimated time to delivery the order and estimated time to travel.


What is that focus of your work today?


I’m currently leading the time prediction area for UberEats. As you can imagine, precise predictions are keys for the system’s efficiency and reliability. The features I’m working on are like predicting how long it will take to deliver the food to the eater, how long will the restaurant spend to prepare the food, etc. Before this, I spent most of my time working on our intelligent dispatch system. Those two things are coupled tightly together, for example, we need to know when the food will be ready before sending the delivery partner to the restaurant to pick it up.


What is the motivation for this talk?


I’m very excited to share how we are using ML to tackle problems, especially in O2O business model since we are pioneers in that industry. We have a three-sided marketplace including delivery partners, restaurants, and eaters. In every decision we make we have to consider all of them in terms of how we are doing the tradeoffs and how we are using ML to figure out the optimal solution. For example, the hardest thing for us to make precise food preparation time prediction is that we don't have the ground truth. A restaurant does not have the incentive or responsibility to tell us how long an order will take to be prepared. But that's the most critical thing for us to figure out the perfect dispatch timing.


How would you describe the persona and the level of the target audience?


People who are interested in solving similar problems with ML in O2O business since it’s not commonly shared across the industry and we figured out a lot the solutions all from scratch.

Speaker: Zi Wang

Leading the Machine Learning Engineering Work for Time Predictions @UberEats

Zi used to work on multiple people online collaboration and co-authoring for Office apps at Microsoft.  After joining Uber in 2015, he’s participated in the design and implementation for multiple products, such as Uber Rush, Uber’s in-house payment system, and Uber Eats. Now he’s leading the machine learning engineering work for time predictions in Uber Eats to improve system efficiency and user experience.

Find Zi Wang at


  • Blameless Culture

    Absorb the lessons learned from failures and outages in a human-centric process.

  • Modern CS in the Real World

    Learn how companies are applying recent CS research to tackle concurrency, distributed data, and coordination.

  • Architectures You’ve Always Wondered About

    Next-gen architectures from the most admired companies in software.

  • Bare Knuckle Performance

    Learn from practitioners on the challenges and benefits of architecting for performance and much more.

  • Java - The Interesting Bits

    Learn the new features in the recent and near-future releases of Java and the JVM and what they offer.

  • Ethical Considerations in Consciously Designed Software

    Design considerations for various contexts, locations, security and privacy requirements.

  • Operating Microservices

    Learn from practitioners operating and evolving systems in performance demanding environments.

  • Security

    Learn how to make security an inherent part of the software development process.

  • Native Compilation Is Back (A Look at Non-Vm Compilation Targets)

    Issues with native compilation for in browser-based and server-side environments

  • Trouble-Shooting in Production

    Learn debugging strategies for complex and high stakes environments where standard debuggers and profilers fail.

  • Predictive Architectures and ML

    Explore the systems and designs covering the full loop from machine learning to inferencing.

  • Data Engineering on the Bleeding Edge

    Explore the latest trends in data engineering that help improve the life of the developer

  • Production Readiness

    Observability, emergency response, capacity planning, release processes, and SLOs for availability and latency.

  • Humane Leadership

    A look at leadership with an emphasis on empathy, taking chances and building other leaders within organizations and teams

  • Devex & Teams

    Explore how to reduce developer friction between teams and stakeholders.