Software Engineer - Machine Learning (SF & PA)

Uber Overview

About us

We're changing the way people think about transportation. Not that long ago we were just an app to request premium black cars in a few metropolitan areas. Now we're a part of the logistical fabric of more than 500 cities around the world. Whether it's a ride, a sandwich, or a package, we use technology to give people what they want, when they want it.

For the people who drive with Uber, our app represents a flexible new way to earn money. For cities, we help strengthen local economies, improve access to transportation, and make streets safer.

And that's just what we're doing today. We're thinking about the future, too. With teams working on autonomous trucking and self-driving cars, we're in for the long haul. We're reimagining how people and things move from one place to the next.

We're bringing Uber to every major city in the world. We need your skills and passion to help make it happen! Be sure to check out the Uber Engineering Blog to learn more about the team.

Job Description

The role

As a Machine Learning Engineer, you'll work on code that improves experiences for riders, driver-partners, and operations teams around the world. Our team works on every aspect of Uber's business, from ridesharing to food delivery to self-driving cars. We're building maps that help move millions. We're working on messaging systems to improve global collaboration. We're building systems that process thousands of payments per second. Join us as we work together to solve some of the today's most interesting computer science and analytics problems.

About you

  • You have at least 5 years experience as a software engineer or in an applied machine learning role
  • You have expertise in one or more object-oriented languages,including Python, Go, Java, or C++, and an eagerness to learn more
  • You have experience with both machine learning and building scalable production services
  • You have experience with distributed storage and database systems, including SQL or NoSQL, MySQL, or Cassandra
  • You have experience using machine learning libraries or platforms, including Tensorflow, Caffe, Theanos, Scikit-Learn,or ML Lib for production or commercial products
  • 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.
  • You have a rock-star-like ability to communicate insights from complex "black-box" models to C-level and working level peers, and the ability to defend algorithm choices to industry experts
  • You have the ability to solve complex business problems and apply machine learning to optimize critical business metrics
  • You follow a strong adherence to metrics driven development, with a disciplined and analytical approach to product development.

Bonus points if

  • You have experience in statistics
  • You enjoy reading academic papers and implementing experimental systems
  • Experience developing complex software systems scaling to millions of users with production quality deployment, monitoring and reliability
  • You have experience presenting at industry recognized ML conferences as well as being published in the field.
  • You have experience in stream processing—Storm, Spark, Flink etc.— and graph processing technologies.

Team-specific focus areas

Additionally, Uber has a variety of roles and teams for you depending on where your interests match best.

  • High performance systems - Experience with building high performance distributed systems that can scale to 100,000s QPS.
  • Core Infrastructure - Experience with developing and running large scale distributed storage systems, service oriented architectures, and reliable monitoring and deployment infrastructure.
  • Data Processing - experience with building and maintaining large scale and/or real-time complex data processing pipelines using Kafka, Hadoop, Hive, Storm, and Zookeeper
  • Machine Learning - experience with machine learning, information retrieval, algorithmic complexity, data mining, pricing, optimization.
  • Geospatial - Familiarity with geospatial datasets and services, such as maps, local search, points of interest and business listings data, mobile device location and GPS traces.

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