\\\\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 produces analytic evidence and insight, as well as applying them to improve technical solutions. \\+ Experience working in a cross-functional and/or cross-business projects, partnering with Product, Scientists, and cross-org leads to shape the team's strategies \\+ Master's degree in Computer Science, Engineering, Mathematics or related field For San Francisco, CA-based roles: The base salary range for this role is USD$171,000 per year - USD$190,000 per year. You will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits. More details can be found at the following link Uber is proud to be an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please let us know by completing this form-

The role requires a Bachelor of Science degree in Statistics, Mathematics, Computer Science, or Machine Learning. A minimum of two years of experience in software engineering is necessary, with a focus on data-driven methodologies, deep learning, and online experimentation. Strong problem-solving skills and expertise in ML methodologies are essential. Experience in applying ML, statistics, or optimization techniques to solve large-scale real-world problems is also required, along with familiarity with ML frameworks like Tensorflow, Pytorch, or JAX, and complex data pipelines using languages such as Python, Spark SQL, Presto, Go, or Java. Preferred qualifications include over three years of experience in software engineering specializing in applied ML methods and experience in designing scalable ML solutions using deep-learning techniques and statistical methods.