Join NVIDIA, a pioneer in computer graphics and AI, as a Senior Manager of Software Engineering. You will spearhead the development of AI-driven employee productivity tools across web, mobile, and agentic platforms. This role offers a unique opportunity to shape the future of how NVIDIA employees work and collaborate globally, leveraging cutting-edge AI.

As a key leader, you will build and nurture a high-caliber engineering team. Your responsibilities will encompass people development, defining product and technical direction, architecting robust systems, and overseeing operational excellence. This is a hands-on leadership position where you'll make a tangible impact on the company's innovative culture.

Lead, mentor, and expand a talented engineering team focused on creating exceptional employee-facing products. Drive the vision, architecture, and multi-quarter roadmap for a comprehensive employee productivity platform serving a worldwide workforce. Collaborate closely with Product, Design, HR, and other critical teams to translate employee needs into actionable priorities and measurable results.

Manage team execution, ensuring effective project prioritization, capacity planning, and milestone tracking for predictable delivery. Provide direct technical leadership across web, mobile, cloud services, and data platforms through design and code reviews. Champion the delivery of personalized experiences powered by LLMs and intelligent agents, establishing best practices for trustworthy AI systems.

A strong foundation in Computer Science, Engineering, or a related field is required, with a Bachelor's, Master's, or PhD. You should possess at least 12 years of overall software engineering experience, including a minimum of 6 years in leadership roles managing engineering teams. A proven ability to hire, retain, and grow technical talent is essential.

Demonstrate success in delivering enterprise-scale, consumer-grade web or mobile products. Experience in defining technical strategy and leading the architecture of distributed, full-stack systems is crucial. A solid understanding of applied AI, including LLMs and agentic systems, is necessary. Familiarity with modern web/mobile development, cloud infrastructure, containers, APIs, CI/CD, data stores, and observability is expected.