Staff Machine Learning Engineer - Pricing

Uber’s Dynamic Supply Pricing team builds the machine learning and pricing algorithms that power real-time driver pricing at marketplace scale. In this Staff Machine Learning Engineering role, you will set technical direction over a 1+ year horizon and deliver end-to-end ML solutions that support distributed systems handling billions of trips., * Own technical roadmap and strategy over the next 1+ year horizon, 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
• Create novel pricing approaches for online marketplaces that combine machine learning, algorithmic game theory, and optimization to improve earnings opportunities for millions of drivers
• Partner with engineers, product managers, and scientists to design and deliver high-impact technical solutions to complex business problems, * Eligible to participate in Uber’s bonus program (all US locations)
• May be offered an equity award and other types of compensation
• Eligible to participate in a 401(k) plan
• Eligible for various benefits

• Ph.D., M.S., or Bachelor’s degree in Computer Science, Machine Learning, or Operations Research, or an equivalent technical background with exceptional demonstrated impact
• 6+ years of experience leading development and deployment of ML models and optimization algorithms in large-scale production environments at top-tier ML companies (for example, 1M+ predictions/sec or 100M+ users), with a track record of strong business impact across multiple quarters
• Proficiency in programming languages such as Python, Scala, Java, or Go
• Experience with large-scale data systems (such as Spark and Ray), real-time processing (such as Flink), and microservices architectures
• Experience across the ML lifecycle at scale, including development, training, productionization, and monitoring (offline pipelines to online serving and MLOps)
• Experience building and deploying pricing algorithms for multi-sided real-time marketplaces involving strategic agent behavior
• Deep understanding of modern ML algorithms (such as DNNs, multi-task models, and transformers) and mathematical optimization (such as multi-objective, LP, and convex optimization)
• Preferred: reinforcement learning and causal machine learning proficiency

Technologies

• Python, Scala, Java, Go
• Spark, Ray, Flink
• Microservices architectures, MLOps
• DNNs, transformers