Staff Machine Learning Engineer - Pricing

About the Role
Uber’s Marketplace is at the heart of Uber’s business and the Dynamic Supply Pricing (DSP) team develops the models, algorithms, signals, and large-scale distributed systems that power real-time driver pricing for billions of rides. Engineers on the team work on cutting-edge marketplace ML problems and real-time multi-objective optimizations serving 1M+ predictions/second. They regularly present $1B+ opportunities to executive stakeholders and receive close mentorship from the most senior engineers within the organization, setting you up for fast-tracked career growth and the opportunity to learn from experienced technical leaders.
We are looking for exceptional ML engineers with a track record of extraordinary impact and with a passion for building large-scale systems that optimize multi-sided real-time marketplaces. In this role, you will lead the design, development, and productionization of advanced ML models and pricing algorithms, covering deep learning, causal modeling, and reinforcement learning. You will work with engineers, product managers, and scientists to set the team’s technical direction and solve some of Uber’s most challenging and most complex business problems in order to provide earnings opportunities for millions of drivers worldwide.
What You Will Do
Drive technical strategy and roadmap ownership over a 1+ year horizon and own the implementation, including platform-level architecture decisions, executive communication and alignment, technical mentorship, and cross-team technical influence
Lead the design, development, and productionization of end-to-end ML solutions for large-scale distributed systems serving billions of trips
Develop novel pricing approaches for online marketplaces combining machine learning, algorithmic game theory, and optimization to provide earnings opportunities for millions of drivers
Work with a team of engineers, product managers, and scientists to design and deliver high-impact technical solutions to complex business problems
Basic Qualifications
Ph.D., M.S. or Bachelor's degree in Computer Science, Machine Learning, or Operations Research, or equivalent technical background with exceptional demonstrated impact
6+ years experience leading the development and deployment of ML models and optimization algorithms in large-scale production environments at top-tier ML companies (e.g. 1M+ predictions/sec or 100M+ users). Track record of delivering outstanding business impact over multiple quarters
Proficiency in programming languages such as Python, Scala, Java, or Go
Proficiency with large-scale data systems (e.g. Spark, Ray), real-time processing (e.g. Flink), and microservices architectures
Proficiency in the development, training, productionization and monitoring of ML solutions at scale, ranging from offline pipelines to online serving and MLOps