Machine Learning Engineer II, Pricing

Join Uber’s Dynamic Supply Pricing team to build machine learning models and pricing algorithms that power real-time driver pricing at massive scale. This role is designed for engineers who want to move from end-to-end model development through production deployment, while partnering with cross-functional teams to solve complex business challenges. The role is based in Sunnyvale, CA (onsite) and offers a base salary of USD 171,000 - 190,000 per year.

What you’ll get

• Bonus eligibility through Uber’s bonus program
• May be offered equity and other compensation types
• 401(k) eligibility for full-time employees
• Eligibility for various benefits

Responsibilities

• Design, develop, and productionize 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 support earnings opportunities for millions of drivers
• Partner with senior engineers on project scope and execution, and mentor junior team members on design and implementation
• Collaborate with engineers, product managers, and scientists to deliver high-impact technical solutions to complex business problems

Requirements

• Ph.D., M.S., or Bachelor’s degree in Computer Science, Machine Learning, or Operations Research, or equivalent technical background with exceptional demonstrated impact
• 2+ years developing and deploying machine learning models and optimization algorithms in large-scale production environments
• Proficiency in Python, Scala, Java, or Go
• Experience with large-scale data systems such as Spark or Ray, real-time processing such as Flink, and microservices architectures
• Experience building, training, productionizing, and monitoring ML solutions at scale, including offline pipelines through online serving and MLOps
• Familiarity with modern ML algorithms (including DNNs, multi-task models, and transformers) and mathematical optimization (including LP and convex optimization), with a demonstrated ability and drive to deepen expertise

Technologies

• Python, Scala, Java, Go
• Spark, Ray, Flink
• Microservices architectures
• MLOps
• DNNs, multi-task models, transformers
• LP, convex optimization

Preferred qualifications

• Experience translating ambiguous business problems into technical solutions in a structured and principled way
• Strong communication skills, including documentation and design discussions
• Experience developing and deploying pricing algorithms for multi-sided real-time marketplaces with strategic agent behavior
• Experience with reinforcement learning and causal machine learning

Location and work setup

Sunnyvale, CA (onsite). Offices remain key to collaboration and Uber’s culture. Unless approved for full remote work, employees must spend at least 50% of their time in-office. Some roles may require full-time in-office presence. Ask your Recruiter for details about this role’s requirements.