Uber's Surge team needs to maintain marketplace reliability by balancing supply and demand in real-time through dynamic pricing, generating billions in annual gross bookings and optimizing network efficiency.
Requirements
- 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 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).
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.
- Build large-scale pricing optimization systems to set prices based on real-time marketplace conditions for Uber's rides products globally.
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 designing and implementing novel metrics for performance evaluation.
- Experience in inference optimization and monitoring model performance efficiency and being able to identify bottlenecks.
