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Staff Machine Learning Engineer - Delivery Courier Pricing

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Uber

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Location:
United States , San Francisco ▼

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Category:
IT - Software Development

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Contract Type:
Not provided

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Salary:

232000.00 - 258000.00 USD / Year
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Job Description:

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 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.

Job Responsibility:

  • 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
  • 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
  • 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
  • Mentor and grow senior ML engineers, establishing technical standards and engineering culture
  • Lead technical discussions and architecture reviews for complex ML systems

Requirements:

  • Bachelors (or higher) in Computer Science, Machine Learning, Operations Research, or related quantitative field
  • 7+ years of experience building and deploying ML models in large-scale production environments
  • Expert-level proficiency in modern ML frameworks and distributed computing platforms
  • 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
  • Technical leadership experience including mentoring senior engineers and driving cross-team technical initiatives

Nice to have:

  • Deep expertise across multiple areas including: Deep Learning, Causal Inference, Reinforcement Learning, Multi-objective Optimization, and Algorithmic Game Theory
  • Experience with feature engineering, model serving, and ML infrastructure at scale (handling millions of predictions per second)
  • 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
What we offer:
  • Eligible to participate in Uber's bonus program
  • May be offered an equity award & other types of comp
  • Eligible to participate in a 401(k) plan
  • Eligible for various benefits

Additional Information:

Job Posted:
February 13, 2026

Employment Type:
Fulltime
Work Type:
Hybrid work
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