Staff Machine Learning Engineer

Uber; San Francisco (CA); On-site; USD 232,000 - 258,000; Full time

Benefits offered by this job

Bonus program

Equity award

401(k) plan

Various benefits

Job summary

Uber is seeking a Staff Machine Learning Engineer for the Rider Pricing team in San Francisco. You will lead a group of Software and Machine Learning Engineers, shaping pricing and optimization models to improve rider pricing in real-time and offline contexts.

You will collaborate across machine learning, serving, and optimization systems to drive Uber’s pricing decisions at massive scale, contributing to increased ridership and revenue while ensuring mobility remains accessible and affordable.

Qualifications

• Master's degree in CS/engineering/math or related field.
• PhD preferred for advanced modeling.
• Experience with ML and optimization algorithms.

Responsibilities

• Lead SWE/MLE team to advance rider pricing models.
• Improve pricing models and promotion algorithms.
• Own end-to-end problems with cross-functional roadmap.
• Mentor junior engineers in ML best practices.
• Collaborate with teams to drive ridership and revenue.
• Support users by enabling affordable mobility.

Skills

C

C++

Java

Python

Go

Machine learning

Optimization algorithms

Education

Master's degree in Computer Science/Engineering/Mathematics/related field

PhD in related field

Tools

Spark

Hive

Job description

The Staff Machine Learning Engineer will join Uber's Rider Pricing team in San Francisco, leading technical direction to advance pricing and optimization algorithms and models. The role focuses on improving rider pricing in both real-time and offline contexts through collaboration across machine learning, serving, and optimization systems.

Role Summary

You will lead a group of Software Engineers and Machine Learning Engineers working on rider pricing. The work supports models and optimization techniques that power pricing decisions at massive scale, contributing to better mobility outcomes for riders and helping drive ridership and revenue growth.

Responsibilities

• Lead a group of SWEs and MLEs to develop and implement new machine learning and optimization techniques supporting Uber's rider pricing for billions of rides worldwide.
• Improve the performance of models and algorithms used in pricing algorithms and promotion targeting.
• Own the end-to-end problem, including partnering with cross-functional teams to define the product and/or technical roadmap.
• Mentor more junior team members by role modeling machine learning best practices.
• Collaborate with cross-functional teams to ensure alignment and drive Uber's ridership and revenue growth.
• Support end-users by making mobility options accessible and affordable.

Required Qualifications

• Master's 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, for example: C, C++, Java, Python, or 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 such as Spark and Hive, plus experience building production-ready algorithmic systems.
• Strong background in deep learning, generative AI, causal modeling, and reinforcement learning.

Technologies

• C, C++, Java, Python, Go
• Spark, Hive

Compensation and Location

• Location: San Francisco, CA (onsite)
• Salary: USD 232,000 - 258,000 per yearly

Benefits

• Eligible to participate in Uber's bonus program
• May be offered an equity award and other types of compensation
• 401(k) plan
• Various benefits

Working Arrangement

Unless approved for full remote work, employees must spend at least 50% of their time in-office. Some roles, including those at greenlight hubs, require full-time in-office presence.

US Plan Eligibility Note

For all US locations, all full-time employees are eligible to participate in a 401(k) plan.