Uber Senior Staff Machine Learning Engineer - 

United States, West Virginia 

338807237

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.

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

• 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

• 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

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.