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.
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.