The NVIDIA Enterprise Product Group builds AI solutions that help enterprises develop, deploy, and scale generative AI, agentic AI, retrieval-augmented generation, and accelerated data workflows from developers laptops to deployed in data centers, clouds, and AI factories. We are looking for a Senior Technical Marketing Engineer focused on Enterprise AI Software, and accelerating adoption of NVIDIA AI software by creating technical content, developer journeys, demos, reference examples, deployment guides, and documentation that make complex systems understandable and actionable.

Act as a bridge between NVIDIA’s enterprise AI software stack and the developers, platform teams, partners, solution architects, and customers who need to build with it. This includes helping audiences understand how NVIDIA AI Enterprise, NIM microservices, Dynamo, NeMo, RAG and agentic AI blueprints, inference platforms, Kubernetes-based deployment patterns, and developer frameworks and libraries fit together across the full stack. We're looking for someone passionate about building scalable AI software, creating excellent technical content, and helping developers adopt cutting-edge technology, At NVIDIA, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join us and see how you can make a lasting impact on the world!

What You'll Be Doing:

Refine developer, user, and agent journeys: Understand how developers, enterprise platform teams, partners, and customers, and their respective agents, consume NVIDIA AI software, then craft clear technical journeys supported by documentation, code examples, demos, and deployment guidance.
Showcase enterprise AI software workflows: Build demos, reference examples, notebooks, and sample applications that show how NVIDIA AI software components work together across model development, inference, RAG, agentic AI, evaluation, deployment, and operations.
Build compelling technical assets: Accelerate adoption by creating public-facing content such as product documentation, deployment guides, reference architectures, tutorials, blog posts, whitepapers, technical presentations, webinars, demo videos, and code examples.
Develop automation and docs-as-code workflows: Create repeatable examples and publishing workflows using Git-based documentation, CI/CD, scripts, templates, and AI-assisted docs or skills where appropriate.
Enable the field and partner ecosystem: Support solution architects, sales teams, cloud partners, ISVs, and ecosystem teams with technical assets that help them explain, deploy, and integrate NVIDIA enterprise AI software.
Collaborate across the stack: Work closely with Technical Marketing Engineering, Product Management, Engineering, Developer Relations, Field, and Marketing teams to turn product capabilities into practical adoption paths.
Capture feedback and improve the product experience: Use customer, partner, developer, and field feedback to identify gaps in usability, examples, documentation, deployment patterns, and product workflows.
Engage the developer and open source community: Advocate for NVIDIA AI software in developer, cloud-native, and open source ecosystems, encouraging adoption through clear examples and practical technical storytelling.

What We Need To See:

BS or MS in Computer Science, Engineering, AI/ML, Data Science, or another technical field, or equivalent experience.
12+ years of proven experience in technical marketing engineering, software development, developer relations, solution architecture, technical writing, product engineering, or a related technical role.
Hands-on experience building, deploying, or explaining AI/ML, generative AI, RAG, agentic AI, LLM-based applications, inference services, or enterprise software workflows.
Experience creating customer-facing technical assets, including product documentation, deployment guides, code examples, tutorials, whitepapers, blog posts, presentations, webinars, or demo videos.
Proven experience with cloud-native software development and deployment patterns, including containers, Kubernetes, Helm, APIs, SDKs, CI/CD, and Git-based workflows.
Strong technical judgment and ability to understand engineering developments, make practical decisions, defen

Senior Technical Marketing Engineer, Enterprise AI Software at NVIDIA