**About the Role**<br /><br />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.<br /><br />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.<br /><br />**What the Candidate Will Do:**<br /><br />- Lead and grow a team of engineers responsible for personalization and ranking capabilities across the shopping journey on the Storefront, Cart, and Checkout surfaces.<br />- Drive execution against high-stakes, highly visible business goals and engineering priorities, ensuring the team delivers reliable, scalable, and measurable impact.<br />- 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.<br />- Partner closely with Product, Design, Data Science, MLE, and partner engineering teams to define and deliver experiences across various shopping features.<br />- Operationalize modern ML capabilities into production systems, helping bridge experimentation and research into robust product experiences.<br />- 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.<br />- Establish strong engineering execution practices across roadmap planning, technical design, prioritization, delivery, operational excellence, and incident management.<br />- Develop engineers and technical leaders on the team through coaching, feedback, and clear growth opportunities.<br /><br />**Basic Qualifications:**<br /><br />- Minimum 3 years of experience managing software engineering teams.<br />- Minimum 10 years of experience in software engineering.<br />- Experience leading teams responsible for complex, distributed, production-grade systems.<br />- Strong technical fluency in machine learning concepts and practical familiarity with ranking systems, recommendation engines, or ML-powered personalization at scale.<br />- Track record of building and evolving scalable platforms, services, and architectures that enable extensibility and reuse by other teams.<br />- Experience hiring, developing, and retaining strong engineering talent while building high-performing teams.<br /><br />**Preferred Qualifications:**<br /><br />- Experience leading teams that own personalization, ranking, recommendations, relevance, merchandising systems, or decisioning platforms.<br />- Experience bringing ML models into large-scale production systems, including model serving, experimentation, monitoring, and iteration loops.<br />- Familiarity with modern approaches such as deep learning, embedding-based retrieval and ranking, and GenAI-driven personalization or recommendation experiences.<br />- Experience building platforms that span multiple user journeys or product surfaces rather than one isolated feature area.<br />- Strong systems thinking with the ability to balance short-term business delivery and long-term platform investment.<br />- Experience working in consumer, marketplace, e-commerce, delivery, or shopping experiences with tight latency and business performance constraints.<br /><br />For San Francisco, CA-based roles: The base salary range for this role is USD$232,000 per year - USD$258,000 per year.<br /><br />For Sunnyvale, CA-based roles: The base salary range for this role is USD$232,000 per year - USD$258,000 per year.<br /><br />For all US locations, you will be eligible to participate in Ubers bonus program, and may be offered an equity award & other types of comp. All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits. More details can be found at the following link [https://jobs.uber.com/en/benefits](https://jobs.uber.com/en/benefits).<br /><br />Ubers mission is to reimagine the way the world moves for the better. Here, bold ideas create real-world impact, challenges drive growth, and speed fuels progress. What moves us, moves the world - lets move it forward, together.<br /><br />Uber is proud to be an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please let us know by completing [this form](https://forms.gle/aDWTk9k6xtMU25Y5A).<br /><br />Offices continue to be central to collaboration and Ubers cultural identity. Unless formally approved to work fully remotely, Uber expects employees to spend at least half of their work time in their assigned office. For certain roles, such as those based at green-light hubs, employees are expected to be in-office for 100% of their time. Please speak with your recruiter to better understand in-office expectations for this role.