Teamblind, Inc.
Sr Staff Machine Learning Engineer - Delivery Courier Pricing Uber 3.7 4d ago Sa
Teamblind, Inc., San Francisco, California, United States, 94199
# Sr Staff Machine Learning Engineer - Delivery Courier Pricing3.75d ago## Job DescriptionAbout The Role
The Courier Pricing team sits within Uber's Delivery Marketplace org and plays a key role in shaping pricing across food, grocery, and other delivery verticals. We work closely with cross-functional teams to develop scalable pricing products that keep our marketplace efficient, reliable, and ready to grow. As a Sr Staff Machine Learning Engineer, you'll build a world-class pricing system that efficiently prices every offer made to Uber's delivery partners-impacting hundreds of millions of consumers and millions of merchants worldwide.
What You Will Do
Technical Leadership & Innovation
• Lead the design and implementation of advanced ML systems for courier pricing algorithms serving millions of couriers
• 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 Courier 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
• 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
• 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) and distributed computing platforms (Spark)
• Deep expertise across multiple areas including: Deep Learning, Causal Inference, Reinforcement Learning, Multi-objective Optimization, and Algorithmic Game Theory
• 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
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 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. #J-18808-Ljbffr
The Courier Pricing team sits within Uber's Delivery Marketplace org and plays a key role in shaping pricing across food, grocery, and other delivery verticals. We work closely with cross-functional teams to develop scalable pricing products that keep our marketplace efficient, reliable, and ready to grow. As a Sr Staff Machine Learning Engineer, you'll build a world-class pricing system that efficiently prices every offer made to Uber's delivery partners-impacting hundreds of millions of consumers and millions of merchants worldwide.
What You Will Do
Technical Leadership & Innovation
• Lead the design and implementation of advanced ML systems for courier pricing algorithms serving millions of couriers
• 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 Courier 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
• 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
• 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) and distributed computing platforms (Spark)
• Deep expertise across multiple areas including: Deep Learning, Causal Inference, Reinforcement Learning, Multi-objective Optimization, and Algorithmic Game Theory
• 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
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 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. #J-18808-Ljbffr