Uber's Surge team aims to maintain marketplace reliability by balancing supply and demand in real-time through dynamic pricing. This involves building scalable systems for market state understanding, demand forecasting, ML predictions, network optimization, and pricing decisions, generating billions in gross bookings and impacting rider experience.
Requirements
- 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 designing and implementing novel metrics for performance evaluation.
- Experience handling time series data and time series forecasting (experience handling spatial temporal data is plus).
Responsibilities
- end-to-end design and implement models for marketplace effects and behaviors
- define relevant metrics and monitoring
- conduct experiments, iterate on models, and identify new opportunities to apply machine learning to our problem space
- build scalable real-time systems to understand the state of the market
- forecast future demand
- 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.
- 3+ years of experience in an ML role with an emphasis on data and experiment driven model development.
- Strong communication skills and can work effectively with cross-functional partners.
- Strong sense of ownership and tenacity toward hard machine-learning projects.
- Proven track record in conducting experiments and tracking models in high-complexity environments.
