Staff Machine Learning Engineer, Rider Pricing & Incentives
Location
Sunnyvale, CA
Job Type
Full-time
Category
other-general
Posted
May 31, 2026
**About the Team**
Uber Marketplace is at the heart of Uber's business, with Rider Pricing & Incentives playing a critical role by setting prices and targeting promotions for Uber riders. Our team's mission is to drive revenue growth, ridership growth and Uber's profitability through advanced machine learning and data science. We build reliable, scalable platforms that optimize rider pricing, real-time and offline promotions, including discounts, and personalized messaging.
The Rider Pricing & Incentives team is a fast-moving, high-opportunity space where you'll have the chance to make a significant impact on the business. You'll take ownership of one key pillar of the promotions or rider pricing domain and lead the technical direction for improving our pricing algorithms, promotion algorithms and models. You will work across ML, serving, and optimization system components, and set technical direction for modeling best practices across model building, evaluation and d...
Uber Marketplace is at the heart of Uber's business, with Rider Pricing & Incentives playing a critical role by setting prices and targeting promotions for Uber riders. Our team's mission is to drive revenue growth, ridership growth and Uber's profitability through advanced machine learning and data science. We build reliable, scalable platforms that optimize rider pricing, real-time and offline promotions, including discounts, and personalized messaging.
The Rider Pricing & Incentives team is a fast-moving, high-opportunity space where you'll have the chance to make a significant impact on the business. You'll take ownership of one key pillar of the promotions or rider pricing domain and lead the technical direction for improving our pricing algorithms, promotion algorithms and models. You will work across ML, serving, and optimization system components, and set technical direction for modeling best practices across model building, evaluation and d...