About the role:
Engages with stakeholders across teams to lead the design, development, optimization, and productionization of machine learning (ML) or ML-based solutions and systems that are used to solve highly complex or vaguely defined problems. This role also leads efforts across teams to leverage and improve ML infrastructure for model development, training, deployment needs and scaling ML systems.
About the Team:
The Marketplace Pricing & Incentives team is responsible for Uber’s profitability and growth story. We focus on how to intelligently price a ride trip or a delivery order to make sure Uber can achieve profitability while keeping a steady growth of business. The team is critical in fostering a healthy ride-sharing & delivery ecosystem via a balanced marketplace and in providing a pleasant and sticky experience for Uber’s customers. We seek the best opportunities to grow Uber's business with the state-of-the-art technology (machine learning, constrained optimization, distributed platform, etc.) to achieve industry leading ROI.
- PhD or equivalent in Computer Science, Engineering, Mathematics or related field AND 2-years full-time Software Engineering work experience OR 5-years full-time Software Engineering work experience, WHICH INCLUDES 3-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 3-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.
- Deep Learning
- Scalable ML architecture
- Feature management