Responsibilities

• 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.

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, 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.

Preferred

• 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.

Benefits

• equity and benefits.