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, defend technical opinions, and translate sophisticated details into useful content.
Excellent written, spoken, and visual communication combined with strong cross-functional collaboration skills, with the ability to balance multiple projects, prioritize under deadlines, and work effectively across engineering, product, field, marketing, and partner teams.
Ways To Stand Out From The Crowd:
Examples of published technical work you authored or built, such as documentation, blogs, tutorials, videos, conference talks, demos, GitHub projects, notebooks, or developer guides.
Experience with NVIDIA AI software or adjacent technologies such as NVIDIA AI Enterprise, NIM, NeMo, TensorRT, Triton Inference Server, RAPIDS, CUDA, AI Blueprints, DGX Cloud, Run:ai, GPU Operator, or Network Operator.
Experience building enterprise-grade generative AI applications, RAG systems, autonomous agents, inference platforms, evaluation workflows, or AI factory software patterns.
Experience working directly with enterprise customers, cloud providers, ISVs, solution architects, sales teams, or partner engineering teams.
NVIDIA is widely considered one of the technology world’s most desirable employers. We have some of the world's most forward-thinking and hardworking people on our team. If you're creative and autonomous, we want to hear from you! NVIDIA benefits is available online at Benefits and Support Programs | NVIDIA Benefits
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 200,000 USD - 322,000 USD.You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Skills Required
- BS or MS in Computer Science, Engineering, AI/ML, Data Science, or another technical field, or equivalent experience
- 12+ years of experience in technical marketing engineering, software development, developer relations, solution architecture, technical writing, product engineering, or 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 (documentation, deployment guides, code examples, tutorials, whitepapers, blogs, presentations, webinars, 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 translate complex engineering details into practical content; excellent written, spoken, and visual communication
- Examples of published technical work (documentation, blogs, tutorials, demos, GitHub projects, notebooks, talks)
- Experience with NVIDIA AI software or adjacent technologies such as NVIDIA AI Enterprise, NIM, NeMo, TensorRT, Triton, RAPIDS, CUDA, DGX Cloud, Run:ai, GPU Operator, Network Operator
- Experience building enterprise-grade generative AI applications, RAG systems, autonomous agents, inference platforms, evaluation workflows, or AI factory patterns
- Experience working directly with enterprise customers, cloud providers, ISVs, solution architects, sales teams, or partner engineering teams
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
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