Used Tools & Technologies
Machine Learning GenAIRequired Skills & Competences
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.
Marketing @ 4
Software Development @ 4
Kubernetes @ 4
GitHub @ 4
CI/CD @ 4
Data Science @ 4
Communication @ 7
Git @ 4
Helm @ 4
Product Management @ 4
API @ 4
LLM @ 4
CUDA @ 4
GPU @ 4
Generative AI @ 4
AI @ 4
Agentic AI @ 4
RAG @ 4
TensorRT @ 4
- 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.
Details
The NVIDIA Enterprise Product Group builds AI solutions that help enterprises develop, deploy, and scale generative AI, agentic AI, retrieval-augmented generation (RAG), and accelerated data workflows from developer laptops to data centers, clouds, and AI factories. This role focuses on 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.
Responsibilities
- Act as a bridge between NVIDIA’s enterprise AI software stack and developers, platform teams, partners, solution architects, and customers.
- Refine developer, user, and agent journeys by crafting technical journeys supported by documentation, code examples, demos, and deployment guidance.
- Showcase enterprise AI software workflows by building demos, reference examples, notebooks, and sample applications that span model development, inference, RAG, agentic AI, evaluation, deployment, and operations.
- Create public-facing technical assets: product documentation, deployment guides, reference architectures, tutorials, blog posts, whitepapers, presentations, webinars, demo videos, and code examples.
- Develop automation and docs-as-code workflows using Git-based documentation, CI/CD, scripts, templates, and AI-assisted documentation where appropriate.
- Enable the field and partner ecosystem by supporting solution architects, sales teams, cloud partners, ISVs, and ecosystem teams with technical assets for explanation, deployment, and integration.
- Collaborate cross-functionally with Technical Marketing Engineering, Product Management, Engineering, Developer Relations, Field, and Marketing teams.
- Capture feedback from customers, partners, developers, and field teams 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 to encourage adoption.
Requirements
- 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 (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 translate engineering developments into practical content.
- Excellent written, spoken, and visual communication and strong cross-functional collaboration skills.
Ways To Stand Out From The Crowd
- Examples of published technical work you authored or built (documentation, blogs, tutorials, videos, conference talks, demos, GitHub projects, notebooks, 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: 200,000 USD - 322,000 USD (final base determined by location, experience, and pay of employees in similar positions).
- Eligible for equity and company benefits (link to NVIDIA benefits provided in the posting).
Other details
- Applications accepted at least until July 11, 2026.
- This posting is for an existing vacancy.
- NVIDIA uses AI tools in its recruiting processes.
- NVIDIA is an equal opportunity employer and committed to an inclusive work environment.