Staff Machine Learning Engineer - Pricing & Incentives
Role summary
Uber is seeking a Staff Machine Learning Engineer for its Pricing & Incentives team within the marketplace domain. This role focuses on optimizing incentive allocation, dynamic trip pricing, and revenue maximization at a global scale. The engineer will collaborate with product, data science, and engineering leadership to define technical roadmaps and problem formulations. Responsibilities include designing, implementing, A/B testing, and productionizing ML models and systems end-to-end. The position requires a PhD or equivalent in a relevant field and at least 4 years of experience building production ML models using statistics and optimization. Preferred qualifications include experience with large-scale data systems, reinforcement learning, personalization, deep learning, causal modeling, and ML platforms.
About The Role
The role will be within the pricing and incentives domain in Uber's marketplace team. The team charter spans incentive allocation and optimization to balance the market and optimize revenue, dynamic trip pricing based on marketplace conditions. The role will provide an opportunity to work on some of the most strategic marketplace problems at Uber scale that impact Uber's global business very directly.
What You Will Do
- Work with product, data science, and eng leadership to shape the technical roadmap and problem formulations for the team.
- Leverage algorithmic knowledge in machine learning/optimization/statistics to design robust engineering solutions to positively impact Uber's business.
- Shape the MLE role and uplevel MLE talents in the org.
- Be responsible for the End to End of the product - ML model pipeline & system design, implementation, AB testing, and rollout. Work with the team to productionize the solutions at scale.
Basic Qualifications:
- PhD or equivalent in Computer Science, Engineering, Mathematics or related field
- 4+ years full-time Machine Learning Engineering work experience in leveraging machine learning/statistics/optimization to build models in production
- Collaborative and work well with, and contribute to, a team
Preferred Qualifications:
- Experience building algorithms with large scale data
- Track record of building large-scale, highly-available systems for both batch and streaming
- Deep domain expertise and are one of the recognized specialists in one or multiple areas like reinforcement learning, personalization, or deep learning.
- Experience in combining observational data with experimental data for building causal models.
- Experience working on large scale Machine Learning platforms
For San Francisco, CA-based roles: The base salary range for this role is USD$232,000 per year - USD$258,000 per year. For Sunnyvale, CA-based roles: The base salary range for this role is USD$232,000 per year - USD$258,000 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. 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 https://jobs.uber.com/en/benefits.
Sample Uber interview questions
- 1
Design a truck tracking system that supports filtering by truck number and includes an interface
system designmedium - 2
Design Uber Eats
system designmedium - 3
Given a sorted array of integers (which may include negatives), return the squares of the numbers
codingmedium - 4
Find the minimum characters to insert to make a string a palindrome
codingmedium - 5
Given an array of integers and a number N, find the length of the longest contiguous subarray such
codingmedium
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