Sr Staff ML Engineer - Surge Pricing at Uber (San Francisco, CA)

| San Francisco, CA
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IC Sr Staff Software Engineer: AI/ML
About the Role:
As a Sr Staff ML Engineer, you will be working on designing and implementing the next generation of the surge pricing algorithm. A few examples are:
  • Design a mechanism to find the best operating point for the surge algorithm based on the market conditions, tune the algorithm, and propose solutions to balance between reliability and trip throughput.
  • Build the next generation of the surge algorithm that learns from its mistakes and optimize the long-term business metrics.
About the Team:
The Surge (Dynamic Pricing) team, within the broader Marketplace group, ensures marketplace reliability when there is an imbalance between supply and demand. Without Surge, trip requests will be unfulfilled once available supplies are used up, leaving the rest of riders stranded regardless of their willingness to pay and to wait. This would lead to unusable Uber experiences.
On the business side, this team generates billions of dollars in gross booking for the company by efficiently balancing between request conversion and rider welfare. On the technology front, this team defines reliability in Uber Marketplace's realtime pricing products, measures the gains and costs in trip throughput and rider welfare, and optimizes them in realtime (before rider shipping session converts), at scale (hundreds of thousands of decisions per second), through a data-driven approach. Specifically, this team collects user intent, processes this data through machine learning models and eventually makes the dynamic pricing decisions for each rider session.
Minimum qualifications:
  • PhD or equivalent in Computer Science, Engineering, Mathematics or related field AND 4-years full-time Software Engineering work experience OR 7-years full-time Software Engineering work experience, WHICH INCLUDES 4-years total technical software engineering experience in one or more of the following areas:
    • Programming language (e.g. C, C++, Java, Python, or Go)
    • Large-scale training using data structures and algorithms
    • Modern machine learning algorithms (e.g., tree-based techniques, supervised, deep, or probabilistic learning)
    • Machine Learning Software such as Tensorflow/Pytorch, Caffe, Scikit-Learn, or Spark MLLib
  • Note the 4-years total of specialized software engineering experience may have been gained through education and full-time work experience, additional training, coursework, research, or similar (OR some combination of these). The years of specialized experience are not necessarily in addition to the years of Education & full-time work experience indicated.
Technical skills:
  • Deep Learning
  • Scalable ML architecture
  • Feature management

  • Privacy aware/bias free/Interpretable ML
  • Personalization
  • Optimization (RL/Bayes/Bandits)
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