Staff ML Engineer - Real-Time Pricing & Scalable ML

Uber; 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’s Dynamic Supply Pricing team builds the machine learning and pricing algorithms that power real-time driver pricing at marketplace scale. In this Staff Machine Learning Engineering role, you will set technical direction over a 1+ year horizon and deliver end-to-end ML solutions that support distributed systems handling billions of trips.

Location: New York, NY onsite. You will own the technical roadmap, collaborate with cross-functional partners, and drive scalable ML models from

Responsibilities

• Own technical roadmap and strategy over the next 1+ year horizon, including platform-level architecture decisions, executive communication and alignment, technical mentorship, and cross-team technical influence
• Lead the design, development, and productionization of end-to-end ML solutions for large-scale distributed systems serving billions of trips
• Create novel pricing approaches for online marketplaces that combine machine learning, algorithmic game theory, and optimization to improve earnings opportunities for millions of drivers
• Partner with engineers, product managers, and scientists to design and deliver high-impact technical solutions to complex business problems

Skills

Ph.D./MS/Bachelor's in CS/ML/OR

6+ years ML production

Python

Scala

Java

Go

Spark

Ray

Flink

Microservices architectures

ML lifecycle

Pricing algorithms

Reinforcement learning

Education

PhD, MS, or Bachelor’s degree in Computer Science, Machine Learning, or Operations Research

Tools

Spark

Ray

Flink

Job description

Uber’s Dynamic Supply Pricing team builds the machine learning and pricing algorithms that power real-time driver pricing at marketplace scale. In this Staff Machine Learning Engineering role, you will set technical direction over a 1+ year horizon and deliver end-to-end ML solutions that support distributed systems handling billions of trips.

Location: New York, NY onsite. You will own the technical roadmap, collaborate with cross-functional partners, and drive scalable ML models from