Uber
Senior Staff Machine Learning Engineer - Driver Pricing & Marketplace Optimizati
Uber, Seattle, Washington, us, 98127
About the Role
We're 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 some of the most complex problems in applied machine learning: real-time pricing optimization, supply-demand balancing, and driver behavior modeling at unprecedented scale. Your work will involve cutting-edge techniques including causal inference, reinforcement learning, algorithmic game theory, and multi-objective optimization to solve challenges that don't exist anywhere else in the industry.
As a Senior Staff ML Engineer reporting directly to the Engineering Director, you'll drive technical strategy, mentor senior engineers, and establish ML engineering excellence across the Driver Pricing organization while solving problems that directly 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 around the world
Architect real-time ML infrastructure handling 1M+ pricing decisions per second with sub-50ms latency requirements
Drive breakthrough research in causal ML, reinforcement learning, algorithmic game theory, and multi-objective optimization for marketplace optimization with strategic agents
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 experimental 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 complex 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
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 areas relevant to 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 Required: 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
Preferred: Personalization and ranking systems at scale
Time series forecasting and demand prediction
Graph-based ML for network effects modeling
Experience with Ads ranking and auction systems
For New York, NY-based roles: The base salary range for this role is USD$257,000 per year - USD$285,500 per year. For San Francisco, CA-based roles: The base salary range for this role is USD$257,000 per year - USD$285,500 per year. For Seattle, WA-based roles: The base salary range for this role is USD$257,000 per year - USD$285,500 per year. For Sunnyvale, CA-based roles: The base salary range for this role is USD$257,000 per year - USD$285,500 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. You will also be eligible for various benefits. More details can be found at the following link https://www.uber.com/careers/benefits. Uber is proud to be an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please let us know by completing this form- https://docs.google.com/forms/d/e/1FAIpQLSdb_Y9Bv8-lWDMbpidF2GKXsxzNh11wUUVS7fM1znOfEJsVeA/viewform
#J-18808-Ljbffr
Architect real-time ML infrastructure handling 1M+ pricing decisions per second with sub-50ms latency requirements
Drive breakthrough research in causal ML, reinforcement learning, algorithmic game theory, and multi-objective optimization for marketplace optimization with strategic agents
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 experimental 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 complex 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
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 areas relevant to 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 Required: 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
Preferred: Personalization and ranking systems at scale
Time series forecasting and demand prediction
Graph-based ML for network effects modeling
Experience with Ads ranking and auction systems
For New York, NY-based roles: The base salary range for this role is USD$257,000 per year - USD$285,500 per year. For San Francisco, CA-based roles: The base salary range for this role is USD$257,000 per year - USD$285,500 per year. For Seattle, WA-based roles: The base salary range for this role is USD$257,000 per year - USD$285,500 per year. For Sunnyvale, CA-based roles: The base salary range for this role is USD$257,000 per year - USD$285,500 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. You will also be eligible for various benefits. More details can be found at the following link https://www.uber.com/careers/benefits. Uber is proud to be an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please let us know by completing this form- https://docs.google.com/forms/d/e/1FAIpQLSdb_Y9Bv8-lWDMbpidF2GKXsxzNh11wUUVS7fM1znOfEJsVeA/viewform
#J-18808-Ljbffr