Sr Staff Machine Learning Engineer- Consumer Incentives

About the role and team

Operating across key business areas like grocery and food, the Consumer Incentives team drives Uber's growth and long-term profitability. We deliver products that create seamless, affordable, and enjoyable customer experiences by building AI/ML-powered intelligence and sophisticated distributed systems at scale for hundreds of millions of global users.

What you’ll do

As a Senior Staff Machine Learning Engineer, you will shape the technical vision and long-term ML strategy for Consumer Incentives. You will tackle fundamental questions about how to personalize incentives, understand their incremental impact on consumer behavior, and optimize investment to deliver customer value and sustainable business growth.

This is a hands-on technical leadership role spanning machine learning, optimization, experimentation, and large-scale systems. You will lead initiatives across team boundaries, partner with engineering and cross-functional leaders to define the roadmap, and translate advances in ML into reliable products with measurable business impact. Building on our established capabilities in deep learning and constrained optimization, you will also help identify and develop opportunities for reinforcement learning, foundation models, and generative AI.

• Set technical direction: Define the long-term ML vision, architecture, and technical roadmap for Consumer Incentives, aligning investments with consumer needs and business priorities.
• Lead strategically important initiatives: Turn ambiguous problems into actionable technical strategies, and drive execution across teams from problem formulation and offline evaluation through experimentation, production deployment, and ongoing improvement.
• Advance personalization and optimization: Design models and algorithms that improve incentive relevance, estimate incremental impact, and optimize decisions under business and operational constraints.
• Architect scalable ML systems: Guide—and contribute directly to—the design and implementation of end-to-end ML systems, spanning data and feature pipelines, model training, inference, and monitoring, with a high bar for reliability, scalability, and maintainability.
• Connect scientific rigor to business outcomes: Establish strong approaches to model evaluation, experimental design, and causal measurement, balancing short-term results with long-term consumer value and sustainable growth.
• Translate emerging techniques into practical advances: Evaluate where reinforcement learning, foundation models, and generative AI can meaningfully improve our products, and lead promising approaches from exploration to production.
• Build alignment across functions: Partner with product management, data science, operations, and engineering leaders to prioritize opportunities, make technical tradeoffs, and deliver shared outcomes.