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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 earnings 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 Machine Learning Engineer to help build and scale the technical foundations behind Uber's driver incentive systems. You will be responsible for developing and productionizing large-scale ML models and decision systems that power both scheduled and near real-time incentive generation. In this role, you will collaborate with senior engineers, product managers, and data scientists to implement technical solutions, navigate trade-offs, and maintain reliable production systems. Your work will directly impact marketplace efficiency and empower earning opportunities for millions of drivers worldwide.
Job Responsibility:
Build, productionize, and maintain ML solutions and data pipelines for the large-scale systems that power Uber's driver incentives
Implement and iterate on advanced ML and optimization techniques to improve marketplace efficiency and reliability, directly impacting the earning opportunities of millions of drivers
Translate business requirements into actionable technical tasks and practical, production-ready code, navigating technical trade-offs to ensure system reliability
Develop a deep understanding of incentives, pricing, and marketplace dynamics to build systems that align with operational needs and business goals
Contribute to high engineering standards by participating in design and code reviews, maintaining robust testing, and ensuring the stability of production ML systems
Partner closely with engineers, product managers, and scientists to ensure the successful delivery of high-impact solutions to marketplace problems
Own technical workstreams from development through production rollout, ensuring consistent execution and measurable impact on your immediate team's goals
Requirements:
Bachelor's or M.S. degree in Computer Science, Statistics, Mathematics, Machine Learning, Operations Research, or a related technical field, or equivalent practical experience
2+ years of experience in software engineering with an emphasis on data-driven methodologies, deep learning, and online experimentation
Solid understanding of machine learning and statistical techniques, including deep learning (e.g., multi-task learning), tree-based models, and experimentation
Proficiency in at least one production-grade language (Python, Scala, Java, or Go) and familiarity with common ML frameworks (e.g., PyTorch, TensorFlow, or scikit-learn)
Solid software engineering fundamentals, including the ability to write clean, maintainable production code, conduct thorough code reviews, and implement testing best practices
Experience operating and monitoring ML models in a production setting, with a basic understanding of MLOps workflows
Strong learning mindset, proactive ownership, and effective communication skills
ability to collaborate effectively within cross-functional teams
Nice to have:
3+ years of experience in software engineering specializing in applied ML methods
Ph.D. in Computer Science, Engineering, Mathematics, or a related field
Proven experience designing and crafting scalable, reliable, and reusable ML solutions using deep-learning techniques and statistical methods
Experience developing or deploying algorithms for pricing, matching, or incentive systems within a two-sided marketplace
Exposure to or experience with multi-armed bandits, reinforcement learning, or causal inference, specifically within production systems
Familiarity with large-scale data and ML infrastructure (e.g., Spark, Flink) and experience working with batch or real-time data processing pipelines
Ability to translate well-defined business problems into actionable ML tasks, prototype ideas quickly, and move projects from conception to production
Experience working on cross-functional or cross-org projects, partnering with Product, Scientists, and leads to shape technical strategies
A detail-oriented, truth-seeking mindset with a focus on producing and valuing analytical evidence to continuously improve technical solutions
What we offer:
Eligible to participate in Uber's bonus program
May be offered an equity award & other types of comp