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.
Basic Qualifications
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.
Preferred Qualifications
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.
For New York City, NY-based roles: The base salary range for this role is USD $232,000 per year - USD $258,000 per year.
For San Francisco, CA-based roles: The base salary range for this role is USD $232,000 per year - USD $258,000 per year.
For Sunnyvale, CA-based roles: The base salary range for this role is USD $232,000 per year - USD $258,000 per year.
For all US locations, you will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits.
