Staff Software Engineer - Engineer

Uber's Marketplace Matching team builds the real-time decision systems that determine how riders and drivers are matched across a global, two-sided marketplace.

These decisions happen under uncertainty, at massive scale, and with competing short- and long-term objectives across marketplace efficiency, reliability, sustainable growth, user value, and quality of experience.

We are looking for a Staff Machine Learning Engineer to help define and build the next generation of matching optimization systems.

This role sits at the intersection of machine learning, sequential decision-making, optimization, control, and marketplace dynamics.

You will own major optimization charters, set technical direction, and lead ambiguous 0→1 problems from formulation through large-scale production deployment.

The work is highly applied: new ideas and novel methods are strongly valued, but the goal is not research for its own sake.

Success means finding the best solution — whether invented, adapted from state of the art, or significantly improved — and making it work reliably in a complex real-world marketplace

What you’ll do

• Set technical direction and own major charters for Uber's real-time matching optimization and ML systems.
• Design matching objectives and decision policies that balance multiple competing marketplace goals across riders, drivers, and the platform.
• Develop and productionize ML-based decision systems for complex sequential and multi-agent environments.
• Apply techniques from reinforcement learning, probabilistic modeling, deep learning, causal inference, and sequential decision-making to large-scale marketplace problems.
• Explore model predictive control and other feedback-control approaches for dynamically adapting matching behavior as marketplace conditions evolve.
• Build behavioral models that capture rider and driver responses to marketplace decisions and incorporate those responses into optimization.
• Develop simulation, experimentation, and causal measurement frameworks to evaluate policies and understand long-term system effects.
• Lead ambiguous 0→1 technical efforts, from problem formulation and modeling through production inference and system integration.
• Identify opportunities where new algorithms or modeling approaches can materially improve marketplace performance, while pragmatically adapting proven techniques when they are the better solution.
• Work closely with engineering, applied science, economics, product, and operations teams to translate complex marketplace problems into scalable technical solutions.
• Mentor senior engineers and raise the technical bar for ML and optimization across the broader matching organization.

• Bachelor's degree in Computer Science, Machine Learning, Engineering, Mathematics, Statistics, or a related quantitative field, or equivalent practical experience.
• 8+ years of industry experience in machine learning, or 6+ years with a PhD or equivalent demonstrated technical depth.
• Significant experience building large-scale production ML systems, including online inference.
• Deep expertise in modern machine learning, including deep learning, reinforcement learning and the principles underlying decision-making under uncertainty.
• Demonstrated ability to formulate ambiguous business or system problems as tractable ML or decision problems and drive them from concept to production. Strong BE programming and systems skills, with the ability to personally design and implement production-quality ML and optimization systems.
• Experience leading technically complex, cross-functional initiatives and influencing engineering or scientific direction beyond an individual project.

• PhD in Machine Learning, Reinforcement Learning, Control, Optimization, Robotics, Operations Research, Statistics, or a closely related field.
• Deep experience with reinforcement learning, optimal control, or related sequential decision-making methods.
• Background in large-scale decision systems such as (two-sided) marketplace optimization, robotics/autonomy, ads optimization, ranking, or recommendation systems.
• Strong understanding of marketplace economics, incentives, behavioral responses, and the dynamics of two-sided platforms.
• Experience designing multi-objective or multi-agent decision systems with complex interactions and long-term effects.
• Experience with causal inference and rigorous online or offline evaluation of ML-driven policies.
• Experience with large-scale simulation environments for policy development and evaluation.
• Track record of introducing novel modeling or algorithmic approaches that materially improved a production system.