**About the Teams:** **ML Foundations:** The ML Foundations team in Uber Eats is deeply engaged in foundational work that impacts many products within the organization. Our team develops key modeling artifacts critical for our business, including entity classifications, entity resolution, attribute enrichments, semantic similarity and complementary recommendation models, and user profiles. We adopt cutting-edge, robust machine learning building blocks for Uber Eats . **Marketplace Consumer Incentives:** The Marketplace Consumer Incentives team is largely responsible for Uber's profitability and growth through the most efficient incentive structures across all business lines like Uber Rides and Uber Eats. The team heavily invests in machine learning, causal inference, constrained optimization, distributed system, etc to optimize/personalize incentive structures to increase consumer engagement. **Shopping Ranking** : The Shopping Ranking Team's mission is enabling eaters to effortlessly make shopping decisions and find what they need. We pursue this mission via an ML-driven algorithmic approach, applying state-of-the-art Machine Learning (ML), Optimization techniques to learn from massive datasets Uber has, and build a scalable and reliable shopping intelligence ranking and recommendation systems. **Merchant Pricing:** The Merchant Pricing team in delivery marketplace is to focused on ensuring the best merchant selection for consumers, innovating on pricing models for merchants to effectively participate the marketplace to achieve their business objective with highest ROI while aligning the Uber's Delivery business by driving growth and profitability. ---- What the Candidate Will Do ---- + Build world-class, large-scale incentives ML systems, and pioneer on end-to-end user incentives experience. + Innovate and productionize start-of-the-art incentives ML models, and customize for Uber's use cases. + Collaborate with cross-functional incentive stakeholders, and drive user incentive experience innovations ---- Basic Qualifications ---- + 2 years minimum of industry experience with a PhD in relevant fields (CS, EE, Math, Stats, Physics, etc.) or 5 years minimum of industry experience with a Masters Degree in a relevant field. + Experience building and productionizing innovative end-to-end Machine Learning systems. + Experience with ML frameworks such as PyTorch and TensorFlow. + Expertise in deep learning, recommendation systems, or optimization algorithms. + Proficiency in one or more coding languages such as Python, Java, Go, or C++. + Experience with any of the following: Spark, Hive, Kafka, Cassandra. ---- What the Candidate Will Do ---- + Experience in Machine Learning, Statistics, Optimization, and/or Data Mining. + Experience in simplifying/converting business problems into ML problems. + Experience developing complex software systems scaling to millions of users with production quality deployment, monitoring and reliability. + Experience presenting at industry recognized ML conferences and a good publishing record For San Francisco, CA-based roles: The base salary range for this role is USD$185,000 per year - USD$205,500 per year. For Sunnyvale, CA-based roles: The base salary range for this role is USD$185,000 per year - USD$205,500 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. 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://docs.google.com/forms/d/e/1FAIpQLSdb_Y9Bv8-lWDMbpidF2GKXsxzNh11wUUVS7fM1znOfEJsVeA/viewform