Uber Sr Manager - Machine Learning Applied AI 

United States, West Virginia 

623990044

About the Role

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.

• 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.

• 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.

• 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.

* 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 .