About the Role

Ads is one of the fastest growing lines of businesses at Uber. Every day thousands of advertisers use our Advertising platform to reach users on the Uber platform to help grow their businesses. The Science team on Ads Delivery designs and builds the core algorithmic components of the Ads Delivery system.

As a Staff Machine Learning Engineer on the team, you will work on understanding how various parts of the system (e.g. auction, pacing, bidding, ranking) are performing. You will lead the design and implementation of new algorithms to make our Ads Delivery system more efficient and performant.

We are looking for experienced candidates, who have had experience building Ads Delivery systems to help accelerate our growth. The ideal candidate should possess a strong passion for understanding complex systems, have the curiosity to understand why systems behave in certain ways, have the drive to research / propose new system designs and is a pragmatist.

What the Candidate Will Need / Bonus Points

---- What the Candidate Will Do ----

1. Build statistical, optimization, and machine learning models for a range of applications in the Ads Delivery space (e.g. auction, bidding, pacing, ranking).
2. Design and execute product experiments and interpret the results to draw detailed and actionable conclusions.
3. Use data to understand product performance and to identify improvement opportunities.
4. Collaborate with cross-functional teams across disciplines such as product, engineering, and marketing to drive system development end-to-end from ideation to productionization.

---- Basic Qualifications ----

1. Ph.D., M.S., or Bachelors degree in Statistics, Economics, Machine Learning, Operations Research, or other quantitative fields .
2. 6+ years of industry experience as an Applied Scientist/Machine Learning Engineer.
3. Knowledge of underlying mathematical foundations of statistics, machine learning, optimization, economics, and analytics.
4. Experience in experimental design and analysis.
5. Experience with exploratory data analysis, statistical analysis and testing, and model development.
6. Ability to use Python to work efficiently at scale with large data sets.
7. Proficiency in SQL.

---- Preferred Qualifications ----

1. Experience in algorithm development and prototyping.
2. Experience in building Ads Delivery systems.
3. Experience with productionizing algorithms for real-time systems.
4. Excellent communication and presentation skills.

For New York, NY-based roles: The base salary range for this role is USD$218,000 per year - USD$242,000 per year.

For San Francisco, CA-based roles: The base salary range for this role is USD$218,000 per year - USD$242,000 per year.

For Seattle, WA-based roles: The base salary range for this role is USD$218,000 per year - USD$242,000 per year.

For Sunnyvale, CA-based roles: The base salary range for this role is USD$218,000 per year - USD$242,000 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. You will also be eligible for various benefits. More details can be found at the following link .

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 .

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.