Sr Staff Machine Learning Engineer- Consumer Incentives
uberSeattle (WA)
Sr Staff Machine Learning Engineer- Consumer Incentives
Posted today
uberSeattle (WA)
SENIORITY
Manager
About the role
Overview:
In this Senior Staff ML Engineer role, you shape the long-term ML vision and strategy for Uber’s Consumer Incentives. You will tackle how to personalize incentives, estimate incremental impact, and optimize investments to drive customer value and growth. The position blends hands-on ML work with technical leadership across experimentation, optimization, and large-scale systems. You’ll collaborate with product, data science, and engineering leaders to translate ML advances into reliable products. A strong hook is shaping incentives at scale to impact hundreds of millions of users. Compensation / Benefits bonus program equity 401(k) various benefits
Responsibilities:
Define long-term ML vision, architecture, and roadmap for Consumer Incentives Lead strategically important initiatives from problem formulation to production deployment Advance personalization and optimization to improve incentive relevance and impact Architect end-to-end scalable ML systems (data pipelines, training, inference, monitoring) Establish robust model evaluation, experimental design, and causal measurement Explore reinforcement learning, foundation models, and generative AI applications Collaborate with product, data science, operations, and engineering to prioritize and trade off opportunities Mentor engineers and leaders to raise technical decision quality and reusable capabilities
Key requirements:
12+ years of ML experience with large-scale deep learning systems Demonstrated ownership of high-impact ML systems (search, recommendations, personalization, targeting) Experience setting technical strategy for a large organization and influencing product roadmaps Strong product intuition linking model improvements to business outcomes mentorship cross-functional collaboration strategic influence deep learning large-scale ML systems optimization and experimentation
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