What are the responsibilities and job description for the Senior Machine Learning Engineer - Rider Pricing & Incentives position at Uber?
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 deployment.
About The Role
In this role, you'll apply advanced machine learning technologies-including deep learning, generative AI for personalized communications, causal modeling, and reinforcement learning-to 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
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 deployment.
About The Role
In this role, you'll apply advanced machine learning technologies-including deep learning, generative AI for personalized communications, causal modeling, and reinforcement learning-to 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
- Take a lead on pricing ML and optimization problems, by developing and implementing 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.
- Masters degree in Computer Science, Engineering, Mathematics, or a related field, with 5 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.
- 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.
Salary : $202,000 - $224,000