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Engineering Manager, Shopping Ranking & Personalization

United States, San Francisco Employment contract 232000.00 - 258000.00 USD / Year · Job Posted May 04, 2026
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Job Description

The Shopping Ranking and Personalization team sits at the center of the Uber Eats shopping experience and is responsible for delivering personalized, relevant, and high-performing content across the Storefront, Cart, Interstitial, and Checkout surfaces. You will lead a team that powers ranking and personalization across core feature areas including storefront carousels, upsells, bundling, add-ons, and other discovery and conversion experiences spanning Storefront, Cart, and Checkout. In this role, you will own both the user-facing personalization strategy and the underlying ranking platform that enables it. You will be responsible for a ranking service that facilitates scoring and serving decisions at scale, while partnering closely with Data Science and MLE teams to bring state-of-the-art models into production, including Deep Learning, GenAI, and embedding-based approaches. This is a highly cross-functional and high-impact leadership role with direct influence on customer experience, conversion, affordability, and merchandising outcomes.

Job Responsibility

  • Lead and grow a team of engineers responsible for personalization and ranking capabilities across the shopping journey on the Storefront, Cart, and Checkout surfaces
  • Drive execution against high-stakes, highly visible business goals and engineering priorities, ensuring the team delivers reliable, scalable, and measurable impact
  • Own the technical and organizational strategy for the ranking platform, including the services and APIs that generate, orchestrate, and serve ranking decisions across multiple surfaces and feature areas
  • Partner closely with Product, Design, Data Science, MLE, and partner engineering teams to define and deliver experiences across various shopping features
  • Operationalize modern ML capabilities into production systems, helping bridge experimentation and research into robust product experiences
  • Build the platform and architectural foundations that allow other teams to extend, compose with, and integrate into ranking and personalization surfaces in a scalable and maintainable way
  • Establish strong engineering execution practices across roadmap planning, technical design, prioritization, delivery, operational excellence, and incident management
  • Develop engineers and technical leaders on the team through coaching, feedback, and clear growth opportunities

Requirements

  • At least 7 years of experience managing software engineering teams
  • At least 15 years of experience in software engineering
  • Experience leading teams responsible for complex, distributed, production-grade systems
  • Strong technical fluency in machine learning concepts and practical familiarity with ranking systems, recommendation engines, or ML-powered personalization at scale
  • Track record of building and evolving scalable platforms, services, and architectures that enable extensibility and reuse by other teams
  • Experience hiring, developing, and retaining strong engineering talent while building high-performing teams
  • Experience leading teams that own personalization, ranking, recommendations, relevance, merchandising systems, or decisioning platforms
  • Experience bringing ML models into large-scale production systems, including model serving, experimentation, monitoring, and iteration loops
  • Familiarity with modern approaches such as deep learning, embedding-based retrieval and ranking, and GenAI-driven personalization or recommendation experiences
  • Experience building platforms that span multiple user journeys or product surfaces rather than one isolated feature area
  • Strong systems thinking with the ability to balance short-term business delivery and long-term platform investment
  • Experience working in consumer, marketplace, e-commerce, delivery, or shopping experiences with tight latency and business performance constraints

Nice to have

  • Experience leading teams that own personalization, ranking, recommendations, relevance, merchandising systems, or decisioning platforms
  • Experience bringing ML models into large-scale production systems, including model serving, experimentation, monitoring, and iteration loops
  • Familiarity with modern approaches such as deep learning, embedding-based retrieval and ranking, and GenAI-driven personalization or recommendation experiences
  • Experience building platforms that span multiple user journeys or product surfaces rather than one isolated feature area
  • Strong systems thinking with the ability to balance short-term business delivery and long-term platform investment
  • Experience working in consumer, marketplace, e-commerce, delivery, or shopping experiences with tight latency and business performance constraints

What we offer

  • Bonus program
  • Equity award
  • 401(k) plan
  • Various benefits

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