Machine Learning Engineer II

About the Role

Uber Marketplace is at the core of Uber's business, and Delivery Pricing 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.This role will drive high-impact projects to optimize pricing at Uber using optimization, machine learning, and causal inference. We are looking for individuals who not only excel in problem solving and critical thinking, but also are interested and proficient in writing production code, converting ideas to scalable systems.

What You Will Do

• Problem Formulation: Partner with Scientists, Product Managers, and stakeholders to translate ambiguous business challenges into concrete ML formulations, system designs, and data requirements
• Build & Deploy: Design, build, and deploy the end-to-end infrastructure and services that power our real-time pricing models at a global scale
• Productionize ML: Own the full production lifecycle of machine learning models. This includes building scalable training/inference pipelines, optimizing models for low-latency serving, and setting up robust monitoring to ensure system health and reliability
• Engineer Data: Develop and maintain the complex feature engineering pipelines that feed our models, working with both batch and near-real-time data sources

Basic Qualifications

• Master's/Bachelor's degree in a similar field with 2+ years of relevant industry experience OR Ph.D. in a quantitative field (e.g., Computer Science, Engineering, Mathematics)
• Strong programming skills e.g. Python and a deep understanding of object-oriented design, data structures, and algorithms
• Solid knowledge of machine learning fundamentals (e.g., model types, feature engineering, evaluation metrics)

Preferred Qualifications

• Hands-on experience building and deploying machine learning models in a production environment
• Experience with high-performance backend languages (e.g., Java, Go, C++) for low-latency microservices
• Experience with large-scale data processing and training systems (e.g., Spark, Hive, Presto)
• Proven ability to work with Product Managers and partner with Scientists to translate research and business needs into scalable engineering solutions
• Excellent communication skills, with the ability to convey complex technical concepts to diverse audiences

For New York, NY-based roles: The base salary range for this role is USD$167,000 per year - USD$185,500 per year.

For San Francisco, CA-based roles: The base salary range for this role is USD$167,000 per year - USD$185,500 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 .

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.