Product Marketing Manager, NVIDIA NIM

Responsibilities

• As a Product Marketing Manager for NVIDIA NIM, you will play a pivotal role in helping enterprises deploy AI. NVIDIA NIM is revolutionizing AI from experimentation to production, packaging optimized, production-ready models for rapid deployment. Our product marketing team operates at the intersection of developer tooling and enterprise go-to-market, ensuring that our narrative resonates with ML engineers and CTOs alike. If you relish diving into technical depth and translating it into compelling stories, this is the perfect opportunity!
• Launching products: You'll build and manage launch plans for high-visibility NIM releases, coordinating assets, timelines, keynote slides, demos, and press materials while driving cross-functional execution with product management, engineering, technical marketing, and PR or equivalent experience.
• You'll align the team on messaging and positioning for NIM across developer and enterprise audiences. You will develop messaging docs, sales enablement materials, customer presentations, solution overviews, and web content.
• Driving awareness: You'll identify target audiences and content gaps, then build the assets that fill them — blogs, webinars, demos, solution briefs, and more.
• Crafting the ecosystem story: You'll drive co-marketing engagements with NVIDIA's model providers, cloud partners, and ISV ecosystem to showcase the full range of possibilities with NIM.
• Excellent written and verbal communication skills, with a proven track record of articulating technical value to both developer and executive audiences.
• More than 8 years proven track record in product marketing, product management, or a similar role within a technology company.
• Highly independent — you manage your own priorities, drive cross-functional alignment, and don't need tactical oversight to complete.
• Ability to collaborate across functions, influence without authority, and drive decisions in a dynamic environment.
• Direct experience with AI inference, model deployment, or developer-facing AI platforms.

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