Machine Learning Engineer II

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 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.

What the Candidate Will Do

• 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.

Basic Qualifications

• 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.

Preferred Qualifications

• 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.

For San Francisco, CA-based roles: The base salary range for this role is USD$171,000 per year - USD$190,000 per year.

For Sunnyvale, CA-based roles: The base salary range for this role is USD$171,000 per year - USD$190,000 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 https://jobs.uber.com/en/benefits.

Uber's mission is to reimagine the way the world moves for the better. Here, bold ideas create real-world impact, challenges drive growth, and speed fuels progress. What moves us, moves the world - let's move it forward, together.

Uber is proud to be an Equal Opportunity 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.

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.