Machine Learning Engineer II

About the Role

The Ads Machine Learning (Ads ML) team at Uber is responsible for providing relevant ad recommendations to the users across the different applications within the Uber ecosystem. We focus on building a deep understanding of both user and merchant behavior to generate accurate ML signals that enhance the Ads auction system. Our goal is to maximize the benefits for both users and merchants within Uber's Ads distribution system.

You will directly impact Uber's Ads systems by defining and executing the Ads ML roadmap, with a focus on enabling and accelerating large-scale improvements to our recommendation systems. Developing relevant, robust, and observable ad recommendations is crucial to Uber’s fast growing Ads Business strategy, making this a highly impactful role.

For New York, NY-based roles: The base salary range for this role is USD$158,000 per year - USD$175,500 per year.

For San Francisco, CA-based roles: The base salary range for this role is USD$158,000 per year - USD$175,500 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 https://www.uber.com/careers/benefits.

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.

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.