**About the Team:** Uber Marketplace (https://marketplace.uber.com/) is at the core of Uber's business, and the Delivery Pricing team is a strategically critical component of Marketplace. The mission of the team is to foster growth and increase profitability of Uber by pushing the frontiers of machine learning, data science, and economics and developing highly reliable and scalable platforms to accelerate Uber's impact on the transportation industry. **About the Role:** Leads efforts within the organization to drive the design, development, optimization, and productionization of ML or ML-based solutions and systems that are used to solve strategically important problems. It is a challenging yet rewarding job. You will have a lot of opportunities to work with product managers, data scientists and of course engineers from other teams. You will participate in the whole development cycle of a software product from product scoping, architecture design, software implementation, to productionisation, and learn how to iterate a product for greater success. Your work will have a direct impact on Uber's top and bottom lines. **What the Candidate Will Do:** - Partner with cross-functional teams to optimize marketplace with ML. - Identify gaps/opportunities and drive improvement of ML application in mission-critical business areas - Design and productionize end-to-end ML solutions to tackle strategically important challenges in Uber's multi-sided marketplace **Basic Qualifications:** - BS or equivalent industry in Computer Science, Machine Learning, Artificial Intelligence, Statistics, or related field. - Minimum 7 years of experience in software development, design & architecture and launching software products. - Experience in applying machine learning models to solve real-world problems. **Preferred Qualifications:** - Master’s degree or PhD in Computer Science, Machine Learning, Artificial Intelligence, Statistics, or related field. - Minimum 5 years of hands-on experience in developing machine learning or deep learning solutions for large-scale real-world problems. - Minimum 3 years of experience working in a complex, matrixed organization involving cross-functional, and/or cross-business projects. - Industry experience in one or more of the following - Personalization, User Understanding, Causal Inference, Reinforcement Learning. - Proven track record of collaboration and leadership, and enthusiastic to take on new problems across the full-stack as we continue to push for innovations. - Excellent communication skills and the ability to collaborate effectively with cross-functional teams. For San Francisco, CA-based roles: The base salary range for this role is USD$252,000 per year - USD$280,000 per year. For Sunnyvale, CA-based roles: The base salary range for this role is USD$252,000 per year - USD$280,000 per year. For all US locations, you will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. You will also be eligible for various benefits. More details can be found at the following link [https://www.uber.com/careers/benefits](https://www.uber.com/careers/benefits). 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](https://forms.gle/aDWTk9k6xtMU25Y5A). Offices continue to be central to collaboration and Uber’s cultural identity. Unless formally approved to work fully remotely, Uber expects employees to spend at least half of their work time in their assigned office. For certain roles, such as those based at green-light hubs, employees are expected to be in-office for 100% of their time. Please speak with your recruiter to better understand in-office expectations for this role.