Engineering Manager II, Machine Learning Rider Pricing & IncentivesMachine Learning, Engineering in Sunnyvale, California

• Full Time

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.

About the Role

In this role, you'll apply advanced machine learning technologiesincluding deep learning, generative AI for personalized communications, causal modeling, and reinforcement learningto optimize pricing strategies and promotional systems.

You will also work with serving infrastructure and product teams to design and evolve the rider pricing and promotions systems to support new product and algorithm evolutions, promotion use cases and drive Uber's top-line rider and revenue growth.

What You'll Do

Manage a group of SWEs and MLEs working on rider pricing and promotions to develop and implement new machine learning and optimization techniques powering billions of rides around the world, and helping riders achieve their mobility needs.

Improve the performance of models and algorithms powering pricing algorithms and promotion targeting.

Own the problem E2E, including working with cross-functional teams to define the product and/or technical roadmap.

Mentor more junior team members by role modeling ML best practices. Collaborate with cross-functional teams to ensure alignment and drive Uber's ridership and revenue growth. Help Uber's end-users by making mobility options accessible and affordable.

Basic Qualifications

• Masters degree in Computer Science, Engineering, Mathematics, or a related field, with 7
• years of full-time engineering experience.

Proficiency in one or more programming languages (e.g., C, C

•

, Java, Python, Go).

Experience with machine learning and optimization algorithms.

Preferred Qualifications

PhD in Computer Science, Engineering, Mathematics, or a related field, with 2

years of full-time engineering experience.

Experience solving complex business problems by translating them into machine learning and optimization solutions.

Familiarity with large-scale data systems (e.g., Spark, Hive) and experience building production-ready algorithmic systems.

Strong background in deep learning, generative AI, causal modeling, and reinforcement learning.

For Sunnyvale, CA-based roles: The base salary range for this role is USD$232,000 per year

USD$258,000 per year.

You will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. All full-time employees are eligible to participate in a 401(k) plan.

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, ancestry, religion, sex, 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.