As a Backend Engineer at Uber, you’ll be working on code that’s closest to the business. Your work will impact live riders, eaters, driver-partners, and operations teams across the globe. Our teams span from the traditional ridesharing business to the new emerging products around UberEATS, our own Mapping technology, and Self-Driving cars.
You will build user-facing products, handle and store thousands of payment transactions per second, and develop intelligent fraud prevention strategies that scale. From building maps that help move millions of people, to the messaging systems that let us interact with them - the work you do will impact every single Uber request.
- Expertise in one or more object oriented programming language (e.g. Python, Go, Java, C++) and the eagerness to learn more
- Experience with developing complex software systems scaling to millions of users with production quality deployment, monitoring and reliability.
- Experience with large-scale distributed storage and database systems (SQL or NoSQL, e.g. MySQL, Cassandra)
- Ability to decompose complex business problems and lead a team in solving them
Bonus points if
- BS/MS/PhD in Computer Science or a related field
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.