Uber is looking to improve the user experience and overall marketplace performance by designing, evaluating, and building promotional products for drivers. This involves solving key business problems such as designing incentive structures, measuring the causal impact of promotions, and creating ML-driven optimizations for marketplace growth.
Requirements
- 2 years of experience in software engineering with an emphasis on data-driven methodologies, deep learning, and online experimentation
- Strong problem-solving skills, with expertise in ML methodologies
- Experience in applying ML, statistics, or optimization techniques to solve large-scale real-world problems (e.g. ads tech, recommender systems)
- Experience in ML frameworks (e.g. Tensorflow, Pytorch, or JAX) and complex data pipelines; programming languages such as Python, Spark SQL, Presto, Go, Java
- 3+ years of experience in software engineering specializing in applied ML methods
- Experience in designing and crafting scalable, reliable, maintainable and reusable ML solutions using deep-learning techniques and statistical methods.
Responsibilities
- Develop and optimize ML models to enhance key marketplace signals (e.g., ETA predictions, supply availability metrics, demand forecasts).
- Collaborate with cross-functional teams (Pricing, Matching, Driver Incentives, etc.) to ensure marketplace signals are effectively utilized.
- Improve operational efficiency by building a centralized, scalable system for marketplace signals that serves multiple use cases.
- Leverage cutting-edge ML techniques (deep learning, probabilistic modeling, reinforcement learning, etc.) to continuously refine marketplace signals.
Other
- Candidates with a passion for solving new and difficult problems with data.
- You will work hand in hand with product, engineering, and operations on new product launches, experiment design and algorithm development.
- We are a fast-moving team and are looking for creative and curious minds to join us!
- Detail-oriented, ownership and truth-seeking mindset.
- Values and produces analytic evidence and insight, as well as applying them to improve technical solutions.
