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Software Engineer - Machine Learning, Uber Eats

Machine Learning, Engineering
in New York, New York

About the Role

UberEverything will collapse the time and distance between everyone and everything they want. Our NYC office focuses on building highly efficient and reliable logistics solutions that span across food delivery (Uber Eats), e-commerce, and more. We are working to define and build the next generation of Uber's logistics platform. You can be a part of that future and build it with us.

The Uber Eats Marketplace team is currently looking for strong Software Engineer with Machine Learning experiences to solve the last-mile delivery logistics problem. We are investing in innovative ML-based product including time prediction, route planning, map improvement and many more.

About you

  • 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, Theano, Scikit-Learn, or Spark ML for production or commercial products
  • Machine learning domain knowledge--bias-variance tradeoff, exploration/exploitation--and understanding of various model families such as decision trees, bayesian models, and deep learning.
  • 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, 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.

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 the curiosity, passion, and collaborative spirit, work with us, and let's move the world forward, together.

Uber is an equal opportunity employer and we value diversity at every level of our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We seek to build a workplace that is inclusive of everyone, and where people from every background can thrive.