Uber
Senior Staff Machine Learning Engineer - Driver Pricing & Marketplace Optimizati
Uber, Seattle, Washington, us, 98127
Senior Staff Machine Learning Engineer - Driver Pricing & Marketplace Optimization
We are seeking an exceptional Senior Staff ML Engineer to lead breakthrough ML innovation in Uber's Driver Pricing organization. This is a high-impact role where you'll architect and build next-generation ML systems that directly optimize marketplace efficiency and driver earnings for millions of drivers globally.
You'll tackle complex problems in applied machine learning including real-time pricing optimization, supply-demand balancing, and driver behavior modeling at unprecedented scale. Your work will involve techniques such as causal inference, reinforcement learning, algorithmic game theory, and multi-objective optimization to solve challenges that don\'t exist elsewhere in the industry. You will report directly to the Engineering Director, drive technical strategy, mentor senior engineers, and establish ML engineering excellence across the Driver Pricing organization while solving problems that impact tens of billions of dollars in marketplace transactions.
What You Will Do
Technical Leadership & Innovation
Lead the design and implementation of advanced ML systems for dynamic pricing algorithms serving millions of drivers across 70+ countries
Architect real-time ML infrastructure handling 1M+ pricing decisions per second with sub-50ms latency
Drive breakthroughs in causal ML, reinforcement learning, algorithmic game theory, and multi-objective optimization for marketplace optimization
Own end-to-end ML model lifecycle from research through production deployment and continuous optimization
Platform & Architecture
Build scalable ML architecture and feature management systems supporting Driver Pricing and broader Marketplace teams
Design experimentation frameworks enabling rapid testing of pricing algorithms using A/B, Switchback, Synthetic Control, and other methodologies
Establish ML engineering best practices, monitoring, and operational excellence across the organization
Create platform abstractions that enable other ML engineers to iterate faster on pricing algorithms
Cross-Functional Impact
Partner with Product, Operations, and Earner Experience teams to translate business requirements into ML solutions
Collaborate with Marketplace Engineering and Science teams to productionize cutting-edge ML research
Work with Platform Engineering teams to ensure ML systems meet reliability and performance standards
Influence technical roadmaps across multiple teams through technical leadership and strategic thinking
Team Development
Mentor and grow senior ML engineers, establishing technical standards and engineering culture
Lead technical discussions and architecture reviews for complex ML systems
Drive knowledge sharing and technical excellence across the Driver Pricing engineering organization
Qualifications Basic Qualifications
PhD in Computer Science, Machine Learning, Operations Research, or related quantitative field OR Master\'s degree with 12+ years of industry experience
10+ years of experience building and deploying ML models in large-scale production environments
Expert-level proficiency in modern ML frameworks (TensorFlow, PyTorch, JAX) and distributed computing platforms (Spark, Ray)
Deep expertise across multiple areas including: Deep Learning, Causal Inference, Reinforcement Learning, Multi-objective Optimization, Algorithmic Game Theory, and Large-scale Ads Ranking/Auction Systems
Proven track record of leading complex ML projects from research through production with significant measurable business impact
Strong programming skills in Python, Java, or Go with experience building production ML systems
Experience with feature engineering, model serving, and ML infrastructure at scale (handling millions of predictions per second)
Technical leadership experience including mentoring senior engineers and driving cross-team technical initiatives
Preferred Qualifications
Marketplace or two-sided platform ML experience with understanding of supply-demand dynamics and pricing mechanisms
Publications or patents in applied machine learning, particularly in optimization, pricing, or marketplace dynamics
Experience with causal inference methodologies and their application to business problems with network effects
Reinforcement learning experience in production environments with long-term optimization and strategic agent considerations
Technical leadership experience including mentoring senior engineers and driving cross-team technical initiatives
Experience with real-time ML systems requiring low-latency inference and high-throughput model serving
Background in economics, operations research, or related quantitative disciplines with application to marketplace problems
Experience with Ads ranking and auction systems with strategic bidding agents and real-time optimization
Technical Skills
Advanced Deep Learning and Neural Network architectures
Scalable ML architecture and distributed model training
Feature engineering and real-time feature serving
ML model deployment, monitoring, and lifecycle management
Statistical analysis and experimental design for ML systems
Causal Machine Learning and causal inference methodologies
Reinforcement Learning and Multi-Armed Bandits
Multi-objective optimization and Pareto efficiency
Algorithmic Game Theory for strategic agent modeling
Notes Location-based base salary ranges apply. For additional details, refer to Uber\'s benefits and compensation documentation.
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You'll tackle complex problems in applied machine learning including real-time pricing optimization, supply-demand balancing, and driver behavior modeling at unprecedented scale. Your work will involve techniques such as causal inference, reinforcement learning, algorithmic game theory, and multi-objective optimization to solve challenges that don\'t exist elsewhere in the industry. You will report directly to the Engineering Director, drive technical strategy, mentor senior engineers, and establish ML engineering excellence across the Driver Pricing organization while solving problems that impact tens of billions of dollars in marketplace transactions.
What You Will Do
Technical Leadership & Innovation
Lead the design and implementation of advanced ML systems for dynamic pricing algorithms serving millions of drivers across 70+ countries
Architect real-time ML infrastructure handling 1M+ pricing decisions per second with sub-50ms latency
Drive breakthroughs in causal ML, reinforcement learning, algorithmic game theory, and multi-objective optimization for marketplace optimization
Own end-to-end ML model lifecycle from research through production deployment and continuous optimization
Platform & Architecture
Build scalable ML architecture and feature management systems supporting Driver Pricing and broader Marketplace teams
Design experimentation frameworks enabling rapid testing of pricing algorithms using A/B, Switchback, Synthetic Control, and other methodologies
Establish ML engineering best practices, monitoring, and operational excellence across the organization
Create platform abstractions that enable other ML engineers to iterate faster on pricing algorithms
Cross-Functional Impact
Partner with Product, Operations, and Earner Experience teams to translate business requirements into ML solutions
Collaborate with Marketplace Engineering and Science teams to productionize cutting-edge ML research
Work with Platform Engineering teams to ensure ML systems meet reliability and performance standards
Influence technical roadmaps across multiple teams through technical leadership and strategic thinking
Team Development
Mentor and grow senior ML engineers, establishing technical standards and engineering culture
Lead technical discussions and architecture reviews for complex ML systems
Drive knowledge sharing and technical excellence across the Driver Pricing engineering organization
Qualifications Basic Qualifications
PhD in Computer Science, Machine Learning, Operations Research, or related quantitative field OR Master\'s degree with 12+ years of industry experience
10+ years of experience building and deploying ML models in large-scale production environments
Expert-level proficiency in modern ML frameworks (TensorFlow, PyTorch, JAX) and distributed computing platforms (Spark, Ray)
Deep expertise across multiple areas including: Deep Learning, Causal Inference, Reinforcement Learning, Multi-objective Optimization, Algorithmic Game Theory, and Large-scale Ads Ranking/Auction Systems
Proven track record of leading complex ML projects from research through production with significant measurable business impact
Strong programming skills in Python, Java, or Go with experience building production ML systems
Experience with feature engineering, model serving, and ML infrastructure at scale (handling millions of predictions per second)
Technical leadership experience including mentoring senior engineers and driving cross-team technical initiatives
Preferred Qualifications
Marketplace or two-sided platform ML experience with understanding of supply-demand dynamics and pricing mechanisms
Publications or patents in applied machine learning, particularly in optimization, pricing, or marketplace dynamics
Experience with causal inference methodologies and their application to business problems with network effects
Reinforcement learning experience in production environments with long-term optimization and strategic agent considerations
Technical leadership experience including mentoring senior engineers and driving cross-team technical initiatives
Experience with real-time ML systems requiring low-latency inference and high-throughput model serving
Background in economics, operations research, or related quantitative disciplines with application to marketplace problems
Experience with Ads ranking and auction systems with strategic bidding agents and real-time optimization
Technical Skills
Advanced Deep Learning and Neural Network architectures
Scalable ML architecture and distributed model training
Feature engineering and real-time feature serving
ML model deployment, monitoring, and lifecycle management
Statistical analysis and experimental design for ML systems
Causal Machine Learning and causal inference methodologies
Reinforcement Learning and Multi-Armed Bandits
Multi-objective optimization and Pareto efficiency
Algorithmic Game Theory for strategic agent modeling
Notes Location-based base salary ranges apply. For additional details, refer to Uber\'s benefits and compensation documentation.
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