Machine Learning Engineer- Matching Inference

Website Uber

Uber’s Marketplace Engineering team creates the technology behind our ridesharing marketplace by connecting riders with drivers at the push of a button. Our solutions expand user access, deliver reliability, and provide more transportation choices to users across our global markets.

About the Role

The matching team directly provides impact to Uber’s growth and profitability by intelligently optimizing dispatch decisions. The team is dealing with a high-scale realtime backend system that’s solving a complex mathematical optimization problem using machine learning.

In 2019, our matching system optimized 1.6 trillion possible pairs and fulfilled 6 billion trips. Though we made some breakthroughs to the system in the past few years, we are still only scratching the surface of the problem. We are looking for a talented software engineer who can move us to the next level. As a software engineer in the Matching Inference team, you will utilize both scalable backend engineering and machine learning skills to make a direct impact on Uber’s mission.

What You’ll Do

  • Translate business level metrics to an engineering/science problem
  • Solving the complicated optimization problem by combining a highly scalable backed system and machine learning models.
  • Be responsible for the End to End of the product – ML model pipeline & backend system design, implementation, AB testing, and rollout.
  • Collaborating in a team environment across all functions, including but not limited to engineers, product managers, data scientists, operations

Basic Qualifications

  • BS/BE degree in Computer Science and related field
  • Experience in one or more object-oriented languages and an eagerness to learn more. We like Python, Go, Java, or C++
  • Demonstrated software engineering experience through previous internships, work experience, coding competitions, projects, and/or publications

Preferred Qualifications

  • Advanced degree in Computer Science and related field.
  • Engineering work, internships, relevant course-work, or project experience in any of the following areas: machine learning, search, ranking, recommendation systems, pattern recognition, data mining, or artificial intelligence
  • Proven experience using machine learning libraries or platforms, including Tensorflow, Caffe, Theanos, Scikit-Learn,or ML Lib
  • Machine learning domain knowledge–bias-variance tradeoff, exploration/exploitation–and understanding of various model families, including neural net, decision trees, bayesian models, instance-based learning, association learning, and deep learning algorithms.
  • Strong engineering and science skills.

About the Team

The Marketplace Dynamics Group (Matching, Surge and Shared Rides), within the broader Marketplace group (, optimizes driver and rider matching algorithms for supply efficiency. We also build real time dynamic pricing mechanisms to balance market reliability and welfare, and identify and explore new growth areas for Uber through the shared rides platform.

At Uber, we ignite opportunity by setting the world in motion. We take on big problems to help drivers, riders, delivery partners, and eaters get moving in more than 600 cities around the world.

We welcome people from all backgrounds who seek the opportunity to help build a future where everyone and everything can move independently. If you have a curiosity, passion and collaborative spirit, work with us, and let’s move the world forward, together.

Uber is proud to be an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements.

If you have a disability or special need that requires accommodation, please let us know by completing this form.

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