**About the Role** Applied AI is a horizontal AI team at Uber collaborating with business units across the company to deliver cutting-edge AI solutions for core business problems. We work closely with engineering, product and data science teams to understand key business problems and the potential for AI solutions, then deliver those AI solutions end-to-end. Key areas of expertise include Generative AI, Computer Vision, and Personalization. We are looking for a Senior Engineering Manager to lead a high-impact team at the intersection of classical machine learning, generative AI, and ML infrastructure. In this role, you’ll be responsible for driving Uber’s next wave of intelligent experiences by managing and scaling a team of world-class engineers delivering ML solutions that power core user and business-facing products. You’ll work cross-functionally with product, data science, research, and platform engineering partners to define strategy, set roadmaps, and ship impactful AI-driven features and systems. The ideal candidate will combine technical depth, execution rigor, and people leadership skills to foster a culture of innovation and excellence. ### **What You’ll Do** - Lead and grow a team of ML engineers focused on solving business-critical problems using a mix of classical ML, deep learning, and generative AI. - Partner with product, science, and engineering leaders to define the technical vision and roadmap for Applied AI initiatives. - Ensure the delivery of high-quality, production-ready ML systems and infrastructure, from experimentation through deployment and monitoring. - Drive adoption of best practices in ML development lifecycle (e.g., data versioning, model training, evaluation, monitoring, responsible AI). - Provide mentorship and career development support to engineers at various levels. - Balance technical innovation with execution — ensuring that your team is both pushing the boundaries of what’s possible and delivering measurable impact. - Represent the team’s work to senior leadership and contribute to org-wide strategic planning. ### **Basic Qualifications** - 12+ years of industry experience in software engineering or machine learning, with 5+ years in a people management role. - Demonstrated success in leading ML or AI-focused teams building and deploying models in production at scale. - Strong technical background in machine learning, deep learning, or AI systems, with fluency in ML infrastructure, MLOps, or platform tooling. - Proven ability to define strategy and align cross-functional stakeholders around a common vision. - Strong execution skills — you’ve led complex projects from idea to production across multiple teams. - Excellent communication, leadership, and interpersonal skills. ### **Preferred Qualifications** - Experience leading team of teams through other managers - PhD in Machine Learning, Computer Science, Statistics, or a related field with research or applied focus on large-scale ML systems. - Prior experience working with generative AI (e.g., LLMs, diffusion models) and integrating such technologies into end-user products. - Experience building and scaling ML platforms or shared ML tooling across multiple teams. - A track record of hiring and developing top ML and engineering talent. We welcome people from all backgrounds who seek the opportunity to help build a future where everyone and everything can move independently. If you have the curiosity, passion, and collaborative spirit, work with us, and let’s move the world forward, together. Offices continue to be central to collaboration and Uber’s 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. \*Accommodations may be available based on religious and/or medical conditions, or as required by applicable law. To request an accommodation, please reach out to [accommodations@uber.com](mailto:accommodations@uber.com).