Staff ML Engineer - Real-Time Marketplace Matching Leader

Uber; San Francisco (CA); On-site; USD 232,000 - 258,000; Full time

Job summary

Uber’s Marketplace Matching team builds real-time decision systems that pair riders and drivers across a two-sided marketplace. This Staff ML Engineer role focuses on defining optimization strategies, owning major charters, and delivering production ML and optimization solutions at scale.

You will apply reinforcement learning, predictive models, and sequential decision-making to improve matching efficiency, reliability, and growth while mentoring engineers and partnering with product, economics,

Qualifications

• 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.

Responsibilities

• 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.

Skills

Production ML systems

Reinforcement learning

Decision-making under uncertainty

Backend engineering

Cross-functional leadership

Education

Bachelor's degree in CS/ML/Engineering/Math

PhD in ML / RL / OR

Job description

Uber’s Marketplace Matching team builds real-time decision systems that pair riders and drivers across a two-sided marketplace. This Staff ML Engineer role focuses on defining optimization strategies, owning major charters, and delivering production ML and optimization solutions at scale.

You will apply reinforcement learning, predictive models, and sequential decision-making to improve matching efficiency, reliability, and growth while mentoring engineers and partnering with product, economics,