Staff ML Engineer: Real-Time Marketplace Optimization

Uber; United States; On-site; USD 232,000 - 258,000; Full time

Benefits offered by this job

Bonuses

Equity

401(k)

Benefits

Job summary

Uber's Marketplace Matching team builds real-time decision systems for rider–driver matching at scale. A Staff Machine Learning Engineer will own major optimization charters, set technical direction, and lead 0→1 problems from formulation through large-scale production deployment.

The role requires applying reinforcement learning, probabilistic modeling, deep learning, causal inference, and sequential decision-making to complex marketplace problems, with collaboration across engineering,

Qualifications

• Bachelor's degree in CS, ML, Eng, Math, or related field or equivalent practical experience.
• 8+ years in ML or 6+ years with a PhD or equivalent depth.
• Experience building large-scale production ML systems with online inference.
• Deep expertise in deep learning, reinforcement learning and decision-making under uncertainty.
• Ability to formulate ambiguous problems as tractable ML/decision problems and ship to production.
• Strong BE programming and systems skills; capable of production-grade ML/optimization systems.

Responsibilities

• Set technical direction and own major charters for Uber's real-time matching optimization and ML systems.
• Design matching objectives and policies balancing multiple marketplace goals across riders, drivers, and the platform.
• Develop and productionize ML-based decision systems for complex sequential and multi-agent environments.
• Apply RL, probabilistic modeling, DL, causal inference, and sequential decision-making to large-scale marketplace problems.
• Explore model predictive control and other feedback-control approaches for dynamic matching behavior.
• Build behavioral models of rider/driver responses and incorporate into optimization.
• Develop simulation, experimentation, and causal measurement frameworks to evaluate policies.
• Lead ambiguous 0→1 technical efforts from problem formulation to production inference.
• Identify opportunities for new algorithms or modeling approaches to improve marketplace performance.
• Collaborate with engineering, applied science, economics, product, and operations teams; mentor senior engineers.

Skills

ML engineering

Reinforcement learning

Optimization

Decision-making

BE programming

Cross-functional leadership

Education

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

PhD in ML/RL/OR/Statistics (preferred)

Job description

Uber's Marketplace Matching team builds real-time decision systems for rider–driver matching at scale. A Staff Machine Learning Engineer will own major optimization charters, set technical direction, and lead 0→1 problems from formulation through large-scale production deployment.

The role requires applying reinforcement learning, probabilistic modeling, deep learning, causal inference, and sequential decision-making to complex marketplace problems, with collaboration across engineering,