Sr Staff Machine Learning Engineer - Delivery Courier Pricing

  • San Francisco, CA
  • Posted 14 hours ago | Updated 2 hours ago

Overview

On Site
USD 257,000.00 - 285,500.00 per year
Full Time

Skills

Innovation
Management
Testing
Operational Excellence
Algorithms
Science
Roadmaps
Strategic Thinking
Team Building
Computer Science
Machine Learning Operations (ML Ops)
TensorFlow
PyTorch
Distributed Computing
Apache Spark
Deep Learning
Game Theory
Research
Python
Java
Publications
Patents
Pricing
Dynamics
Network
Optimization
IT Management
Mentorship
Real-time
Machine Learning (ML)
Economics
Operations Research
Law
Legal
Collaboration

Job Details

About 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

1. Lead the design and implementation of advanced ML systems for courier pricing algorithms serving millions of couriers
2. Own end-to-end ML model lifecycle from research through production deployment and continuous optimization

Platform & Architecture

1. Build scalable ML architecture and feature management systems supporting Courier Pricing and broader Marketplace teams
2. Design experimentation frameworks enabling rapid testing of pricing algorithms using A/B, Switchback, Synthetic Control, and other experimental methodologies
3. Establish ML engineering best practices, monitoring, and operational excellence across the organization
4. Create platform abstractions that enable other ML engineers to iterate faster on pricing algorithms

Cross-Functional Impact

1. Collaborate with Marketplace Engineering and Science teams to productionize cutting-edge ML research
2. Work with Platform Engineering teams to ensure ML systems meet reliability and performance standards
3. Influence technical roadmaps across multiple teams through technical leadership and strategic thinking

Team Development

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

\\-\\-\\-\\- Basic Qualifications ----

1. PhD in Computer Science, Machine Learning, Operations Research, or related quantitative field OR Master's degree with 12+ years of industry experience
2. 10+ years of experience building and deploying ML models in large-scale production environments
3. Expert-level proficiency in modern ML frameworks (TensorFlow, PyTorch) and distributed computing platforms (Spark)
4. Deep expertise across multiple areas including: Deep Learning, Causal Inference, Reinforcement Learning, Multi-objective Optimization, and Algorithmic Game Theory
5. Proven track record of leading complex ML projects from research through production with significant measurable business impact
6. Strong programming skills in Python, Java, or Go with experience building production ML systems
7. Experience with feature engineering, model serving, and ML infrastructure at scale (handling millions of predictions per second)
8. Technical leadership experience including mentoring senior engineers and driving cross-team technical initiatives

\\-\\-\\-\\- Preferred Qualifications ----

1. Marketplace or two-sided platform ML experience with understanding of supply-demand dynamics and pricing mechanisms
2. Publications or patents in applied machine learning, particularly in areas relevant to optimization, pricing, or marketplace dynamics
3. Experience with causal inference methodologies and their application to business problems with network effects
4. Reinforcement learning experience in production environments with long-term optimization and strategic agent considerations
5. Technical leadership experience including mentoring senior engineers and driving cross-team technical initiatives
6. Experience with real-time ML systems requiring low-latency inference and high-throughput model serving
7. 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 [](;br>
Uber's mission is to reimagine the way the world moves for the better. Here, bold ideas create real-world impact, challenges drive growth, and speed fuels progress. What moves us, moves the world - let's move it forward, together.

Uber is proud to be an Equal Opportunity 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](;br>
Offices continue to be central to collaboration and Uber's cultural identity. Unless formally approved to work fully remotely, Uber expects employees to spend at least half of their work time in their assigned office. For certain roles, such as those based at green-light hubs, employees are expected to be in-office for 100% of their time. Please speak with your recruiter to better understand in-office expectations for this role.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.