Role overview
- 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
Basic qualifications
- 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
Preferred qualifications
- 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
Tags & focus areas
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