The Surge team at Uber aims to maintain marketplace reliability by balancing supply and demand in real-time through dynamic pricing. This role is critical to Uber's mission of making transport accessible by optimizing network efficiency, generating billions in annual gross bookings, and significantly contributing to driver earnings.
Requirements
- PhD in relevant fields (CS, EE, Math, Stats, etc.) with a focus on Machine Learning or Masters with 2+ years of experience.
- Experiences in an ML role with an emphasis on data and experiment driven model development.
- Expertise in deep learning and optimization algorithms.
- Experience with ML frameworks such as PyTorch and TensorFlow.
- Experience building and productionizing innovative end-to-end Machine Learning systems.
- Proficiency in one or more coding languages such as Python, Java, Go, or C++.
- Experience designing and implementing novel metrics for performance evaluation.
Responsibilities
- Build and train machine learning models.
- Initiate new areas where machine learning models can make a large impact on the surge algorithm.
- Create a safe ML deployment and observability system.
- Work with a mixed team of Engineers, Operations Researchers, and Economists to build large-scale pricing optimization systems.
- Set prices based on real-time marketplace conditions for Uber's rides products globally.
- Make predictions using ML models.
- Solve network optimization programs.
Other
- PhD in relevant fields (CS, EE, Math, Stats, etc.) with a focus on Machine Learning or Masters with 2+ years of experience.
- Strong communication skills and can work effectively with cross-functional partners.
- Strong sense of ownership and tenacity toward hard machine-learning projects.
- Experience in serving and monitoring online training systems such as real time recommendation systems.
- Experience handling time series data and time series forecasting (experience handling spatial temporal data is plus).
