Real-Time Pricing ML Engineer II

Uber; Sunnyvale (CA); Hybrid; USD 171,000 - 190,000; Full time

Benefits offered by this job

Bonus program

Equity award

401(k) plan

Various benefits

Job summary

Uber’s Dynamic Supply Pricing team is building machine learning systems that power real-time driver pricing at massive scale. In this Machine Learning Engineer II role based in Sunnyvale, you will design, develop, and productionize advanced ML models and pricing algorithms for distributed systems supporting billions of trips.

The team’s work connects machine learning with pricing and optimization methods to create earnings opportunities for millions of drivers in an online marketplace context.

Qualifications

• PhD, MS, or Bachelor's degree in Computer Science, Machine Learning, or Operations Research, or equivalent technical background with exceptional demonstrated impact.
• 2+ years of experience developing and deploying machine learning models and optimization algorithms in large-scale production environments.
• Proficiency in programming languages such as Python, Scala, Java, or Go.

Responsibilities

• Design, develop, and productionize end-to-end machine learning solutions for large-scale distributed systems that serve billions of trips.
• Develop new pricing approaches for online marketplaces that combine machine learning with algorithmic game theory and optimization.
• Collaborate with senior engineers to plan project scope and execution, and mentor junior team members on design and implementation.
• Work alongside engineers, product managers, and scientists to deliver high-impact technical solutions to complex business problems.

Skills

Python

Scala

Java

Go

MLOps

Spark

Ray

Distributed systems

DNNs

Education

PhD/MS/BSc in CS/ML/OR or equivalent

Tools

Spark

Ray

Flink

Job description

Uber’s Dynamic Supply Pricing team is building machine learning systems that power real-time driver pricing at massive scale. In this Machine Learning Engineer II role based in Sunnyvale, you will design, develop, and productionize advanced ML models and pricing algorithms for distributed systems supporting billions of trips.

The team’s work connects machine learning with pricing and optimization methods to create earnings opportunities for millions of drivers in an online marketplace context.