\\\\About the Role\\\\ Uber's Marketplace is at the core of the business. The Earner Incentive team in Marketplace builds products and systems that empower drivers through targeted incentives, creating a more balanced and efficient marketplace while enhancing engagement and experience. The team owns the end-to-end incentive lifecycle, from ML-driven incentive generation to scalable online serving, answering questions such as who, where, when, how, and how much, powered by large-scale machine learning, optimization, and experimentation systems . These systems enable proactive, targeted incentives that shape supply, optimize earnings, and guide marketplace balance. We are seeking a Senior Machine Learning Engineer to design and scale the technical foundations behind Uber's driver incentive systems. You will develop and productionize large-scale ML models and decision systems that power both scheduled and near real-time, intelligent incentive generation and delivery at Uber's global scale. In this role, you will collaborate closely with engineers, product managers, operations, and scientists to set technical direction, make thoughtful trade-offs, and turn complex problems into reliable production systems. Your work will directly shape how incentives are designed and delivered at scale, enhancing marketplace efficiency and reliability, and empowering earning opportunities for millions of drivers worldwide. \\\\What the Candidate Will Do\\\\ \\+ Design, develop, productionize, and operate end-to-end ML solutions and data pipelines for large-scale systems that power driver incentives. \\+ Develop and apply advanced ML and optimization techniques to design incentive mechanisms for online marketplaces, improving marketplace efficiency and reliability while enabling earning opportunities for millions of drivers. \\+ Build deep domain expertise in incentives, pricing, and marketplace dynamics, and understand how these systems interact with Operations. Translate business requirements into clear problem statements and actionable technical plans, reasoning through trade-offs to deliver practical, production-ready solutions. \\+ Help set the team's technical direction and drive execution in partnership with technical leads. Provide technical mentorship, and review designs and code to maintain high engineering quality. \\+ Collaborate closely with engineers, product managers, scientists, and Operations to drive clarity, alignment, and delivery of high-impact solutions to complex business problems. \\+ Own projects end-to-end, from ideation and design through production rollout and iteration, and drive measurable business impact across teams. \\\\Basic Qualifications\\\\ \\+ Ph.D., M.S., or Bachelor's degree in Computer Science, Statistics, Mathematics, Machine Learning, Operations Research, or a related field, or equivalent practical experience with demonstrated impact. \\+ 5+ years of experience across the end-to-end ML lifecycle, including data analysis, feature engineering, model development, deployment, monitoring, and iteration in large-scale production systems. Proven ability to deliver measurable business impact and strong understanding of MLOps best practices. \\+ Strong understanding of a broad range of ML and statistical techniques, including deep learning (e.g., multi-task learning, transformers), tree-based models, and classical approaches, with solid judgment in selecting methods based on context and data. \\+ Proficiency in at least one production language (Python, Scala, Java, or Go) and common ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn). \\+ Solid software engineering skills, including system design, writing and reviewing production-quality code, testing, and operating ML systems in production. \\+ Strong ownership, learning mindset, collaboration and communication skills; able to work independently and effectively in cross-functional teams. \\\\Preferred Qualifications\\\\ \\+ Experience developing and deploying pricing, matching, or incentive algorithms for two-sided marketplaces, with strong product intuition and system-level thinking. \\+ Experience with multi-armed bandits, reinforcement learning, and causal ML, including applying these methods in production systems. \\+ Familiarity with large-scale data and ML infrastructure (e.g. Spark, Flink), and batch or real-time data processing systems. \\+ Strong communication and leadership skills, with the ability to lead initiatives, prototype quickly, drive alignment, and collaborate effectively with cross-functional partners, from early idea generation through productionization. \\+ Experience leading complex technical projects, influencing scope, technical direction, and execution across multiple engineers or teams. \\+ Ability to translate ambiguous business problems into clear, actionable problem statements, define success metrics, and drive execution through well-reasoned trade-offs. \\+ Demonstrated technical leadership, such as mentoring engineers, leading cross-functional efforts, or shaping ML / optimization strategy. \\+ Experience designing, running and analyzing large-scale online experiments to prove impact, interpret results, guide decision-making, and translate insights into concrete product or system changes. For San Francisco, CA-based roles: The base salary range for this role is USD$202,000 per year - USD$224,000 per year. For Seattle, WA-based roles: The base salary range for this role is USD$202,000 per year - USD$224,000 per year. For Sunnyvale, CA-based roles: The base salary range for this role is USD$202,000 per year - USD$224,000 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. 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-

Candidates should possess a Ph.D., M.S., or Bachelor's degree in a related field, or equivalent practical experience. A minimum of 5 years of experience across the end-to-end ML lifecycle is required, including data analysis, feature engineering, model development, deployment, monitoring, and iteration in large-scale production systems. Proficiency in at least one production language such as Python, Scala, Java, or Go, and common ML frameworks like PyTorch, TensorFlow, or scikit-learn is necessary. Strong software engineering skills, including system design, writing production-quality code, testing, and operating ML systems, are also essential. Experience with pricing, matching, or incentive algorithms for two-sided marketplaces is preferred.