Machine Learning Engineer II

Machine Learning Engineer II Department: Engineering Team: Machine Learning Location: San Francisco, United States Type: Full-Time **About the Role** The Marketplace Signals team at Uber is responsible for building and optimizing foundational marketplace signals that power user experiences and drive marketplace efficiency. Our team ensures that key signals—such as eyeball ETA, spinner time, and supply reliability indicators—are leveraged effectively across various Uber products and levers, enabling data-driven decision-making and seamless coordination across different business functions. **What You'll Do** - 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. **Basic Qualifications** - B.S. in Statistics, Mathematics, Computer Science, or Machine Learning - 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 **Preferred Qualifications** - 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. - Detail-oriented, ownership and truth-seeking mindset. - Values and