Senior Technical Marketing Engineer, Enterprise AI Software

at Nvidia

USD 200,000-322,000 per year

SENIOR

✅ On-site

Tech Stack
Tag name is followed by "@" symbol and proficiency level value. About proficiency levels:

1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;

3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;

7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;

10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.

AI @ 4 API @ 4 Agentic AI @ 4 CI/CD @ 4 CUDA @ 4 Communication @ 7 Data Science @ 4 GPU @ 4 GenAI Generative AI @ 4 Git @ 4 GitHub @ 6 Helm @ 4 Kubernetes @ 4 LLM @ 4 Machine Learning Marketing @ 4 Microservices RAG @ 4 Software Development @ 4 TensorRT @ 4

Details

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 developer laptops to data centers, clouds, and AI factories.

This role focuses 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.

The position acts as a bridge between NVIDIA's enterprise AI software stack and developers, platform teams, partners, solution architects, and customers. Relevant technologies include NVIDIA AI Enterprise, NIM microservices, Dynamo, NeMo, RAG and agentic AI blueprints, inference platforms, Kubernetes-based deployment patterns, and developer frameworks and libraries.

Responsibilities

Refine developer, user, and agent journeys by understanding how developers, enterprise platform teams, partners, and customers consume NVIDIA AI software, then creating clear technical journeys supported by documentation, code examples, demos, and deployment guidance.
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.
Create public-facing technical assets, including product documentation, deployment guides, reference architectures, tutorials, blog posts, whitepapers, technical presentations, webinars, demo videos, and code examples.
Develop repeatable examples and docs-as-code publishing workflows using Git-based documentation, CI/CD, scripts, templates, and AI-assisted documentation or skills where appropriate.
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 with Technical Marketing Engineering, Product Management, Engineering, Developer Relations, Field, and Marketing teams to turn product capabilities into practical adoption paths.
Use customer, partner, developer, and field feedback to identify gaps in usability, examples, documentation, deployment patterns, and product workflows.
Advocate for NVIDIA AI software in developer, cloud-native, and open-source ecosystems through clear examples and practical technical storytelling.

Requirements

BS or MS in Computer Science, Engineering, AI/ML, Data Science, another technical field, or equivalent experience.
12 or more 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 the ability to understand engineering developments, make practical decisions, defend technical opinions, and translate sophisticated details into useful content.
Excellent written, spoken, and visual communication skills, combined with strong cross-functional collaboration abilities.
Ability to balance multiple projects, prioritize under deadlines, and work effectively across engineering, product, field, marketing, and partner teams.

Preferred Qualifications

Published technical work 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.

Compensation and Benefits

Base salary range: USD 200,000–322,000 per year.
Eligibility for equity and benefits.
NVIDIA benefits are available through its Benefits and Support Programs.

Applications for this job will be accepted at least until July 11, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes and is an equal opportunity employer.