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Machine Learning Enginner II - Marketplace Pricing & Incentives

Engineering
San Francisco, California |
Sunnyvale, California

About the Role

Uber Marketplace is at the core of Uber's business, and Mobility Simulation and Planning is a critical component of Marketplace. The team's mission is to drive the growth and efficiency of the marketplace while optimizing revenue through pricing and incentives simulation and optimization. The role will provide an opportunity to work on some of the most challenging marketplace problems at Uber's scale that directly impact Uber's global business.

What the Candidate Will Need / Bonus Points

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

In this role, you will develop and implement ML and optimization solutions to enhance pricing and incentive efficiency, while optimizing interactions with other marketplace components.

Key responsibilities include:

  1. Leading the design and implementation of ML-driven solutions to meet business requirements.
  2. Managing end-to-end project execution, from scoping and offline evaluation to experimentation, production, and post-launch monitoring.
  3. Developing and refining ML models and optimization algorithms to improve simulation accuracy and overall performance.
  4. Collaborating with cross-functional teams, including product, operations, and science partners.

---- Basic Qualifications ----

  1. 2+ years of experience in an ML/optimization role, or a PhD in a relevant field (CS, OR, EE, Statistics, etc.)
  2. Expertise in machine learning and optimization algorithms
  3. Experience with ML frameworks
  4. Proficiency in at least one coding language such as Python, Go, or Java
  5. Strong communication skills and ability to work effectively with cross-functional partners
  6. Strong sense of ownership to drive projects end-to-end

---- Preferred Qualifications ----

  1. Experience in translating ambiguous business problems into structured, principled technical solutions
  2. Experience in developing and deploying optimization algorithms in production
  3. Experience in causal inference and experimental design
  4. Experience in evaluating ML models in a production environment

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 Sunnyvale, 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 https://www.uber.com/careers/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.


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Uber is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, Veteran Status, or any other characteristic protected by law.