Senior Machine Learning Engineer - Trip Pricing Optimization at Uber (San Francisco, CA)

| San Francisco, CA
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Uber's Marketplace Engineering team creates the technology behind our Ridesharing and Delivery 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 role will be within the pricing and incentives domain in Uber's marketplace team. The team charter spans incentive allocation and optimization to balance the market and optimize revenue, dynamic trip pricing based on marketplace conditions. The role will provide an opportunity to work on some of the most strategic marketplace problems at Uber scale that impact Uber's global business very directly.
About the Team
This team brings in the innovation, power of scale, reliability and automation to the Marketplace Pricing ecosystem. The team focuses on Applied Machine Learning and is responsible for innovating on and scaling the state-of-the-art Machine Learning / Deep Learning models and Pricing algorithms to run at Uber's global scale.
The team builds the real-time platforms, machine learning pipelines and simulation systems to help achieve Uber's market goals (e.g. increasing throughput with minimal impact to reliability and quality of the service). The challenges range from building real-time and offline distributed data processing pipelines for Feature Engineering and Automation as well as building systems and frameworks to deploy, scale, manage and monitor Real-time ML models and Pricing algorithms. These models power several products at Uber including Real-time Pricing, Demand Shaping and Supply Positioning.
What You'll Do
As a Machine Learning engineer on this team, you'd have an opportunity to:
  • Drive Machine learning efforts and innovation in Pricing and Incentives,
  • Deliver on cross-team projects spanning across multiple lines of business,
  • Design and build ML pipelines to launch ML / DL models at Uber's global scale, and
  • Work with cross-functional stakeholders across Senior and Staff Engineers, Researchers, Product and Data Science to solve complex problems to help Uber achieve market goals (reliability, growth, profit, etc.).

Basic Qualifications
  • 5+ Years of industry experience with a Masters or Bachelors degree in relevant fields (CS, Stats, etc.).
  • 3+ Years on Machine Learning, Statistics, Optimization and Data Mining.
  • Led cross-functional teams to translate vaguely defined business problems into ML problems.
  • Expertise in one or more object-oriented languages, including C++, Java, Python, Go or Scala.
Preferred Qualifications
  • Strong understanding of linear/convex optimization, primal-dual methods and other computational techniques, and applying it to real world problems.
  • Experience with experimentation and ability to interpret the results and iterate.
  • Experience developing complex software systems scaling to millions of users with production quality deployment, monitoring and reliability.
  • Experience working with Data Processing technologies such as Hive, Spark, Flink, etc. and ML frameworks such as SKLearn, Tensorflow, etc.
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