Staff ML Engineer: Real-Time Rider Pricing & Optimization

Uber Technologies Inc.; New York (NY); On-site; USD 232,000 - 258,000; Full time

Benefits offered by this job

Bonus program

Equity award

401(k) plan

Benefits

Job summary

Uber Technologies Inc. is seeking a senior engineer to own and advance rider pricing systems that power real-time and offline pricing, promotions, and discounts.

You’ll address ML and optimization challenges across modeling, serving, and product fronts, guiding the technical direction and best practices to scale pricing for billions of rides globally. The role involves leading a team of software engineers and ML engineers, mentoring peers, and collaborating with cross-functional partners to

Qualifications

• Masters degree in Computer Science, Engineering, Mathematics, or a related field plus 7+ years of full-time engineering experience.
• Proficiency in one or more programming languages (C, C++, Java, Python, Go).
• Experience with machine learning and optimization algorithms.

Responsibilities

• Lead a group of SWEs and MLEs to develop and implement ML and optimization techniques powering billions of rides.
• Improve the performance of models and algorithms powering pricing and promotions.
• Own the problem end-to-end, define product/technical roadmaps with cross-functional teams.
• Mentor junior team members by modeling ML best practices and collaborate across teams.

Skills

C/C++

Java

Python

Go

Machine learning

Optimization

Education

Master's degree in CS/Engineering/Math

PhD in CS/Engineering/Math

Tools

Spark

Hive

Job description

Uber Technologies Inc. is seeking a senior engineer to own and advance rider pricing systems that power real-time and offline pricing, promotions, and discounts.

You’ll address ML and optimization challenges across modeling, serving, and product fronts, guiding the technical direction and best practices to scale pricing for billions of rides globally. The role involves leading a team of software engineers and ML engineers, mentoring peers, and collaborating with cross-functional partners to