Senior Technical Marketing Engineer, Enterprise AI Software

Nvidia

Confirmed live today High trust

Quick summary

Work type: On-site
Location: Santa Clara, CA
Salary: $200,000–$322,000 / yr
Employment: Full-time
Posted: 64 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $202k

This role $261k

$141k $341k

below market most similar roles pay here above market

This role pays more than 85% of similar roles. Most pay $167,300–$235,750 — the blue band above. At the midpoint, this role pays about $261k versus about $202k for comparable roles.

Based on 240 similar postings.

Employer

About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 1623 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 1110 roles with salary data.

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At a glance

TL;DR · Senior Technical Marketing Engineer, Enterprise AI Software

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The Senior Technical Marketing Engineer, Enterprise AI Software joins the NVIDIA Enterprise Product Group to accelerate the adoption of enterprise AI software. This role involves creating technical content, developer journeys, demos, reference examples, and deployment guides that make complex systems actionable for developers, platform teams, and partners. You will build notebooks, sample applications, and public-facing assets like whitepapers and tutorials to showcase workflows for model development, inference, RAG, and agentic AI. Key technologies include NVIDIA AI Enterprise, NIM, NeMo, TensorRT, Triton Inference Server, RAPIDS, CUDA, and Kubernetes-based deployment patterns using Helm, APIs, SDKs, and CI/CD. You will solve the challenge of translating sophisticated engineering developments into practical adoption paths across the full stack, from local laptops to data centers and AI factories.

Skills

Generative AI RAG Agentic AI LLM Kubernetes CI/CD Git NVIDIA AI Enterprise NIM NeMo TensorRT Triton Inference Server RAPIDS CUDA Helm APIs SDKs Cloud-native Docs-as-code

What you'll do

Craft clear technical journeys for developers and enterprise teams using documentation, code examples, and deployment guidance.
Build demos, reference examples, notebooks, and sample applications showcasing NVIDIA AI software workflows.
Create public-facing technical assets including whitepapers, tutorials, blog posts, webinars, and demo videos.
Develop automation and docs-as-code workflows using Git-based documentation, CI/CD, and scripts.
Provide technical assets to solution architects, sales teams, and cloud partners to support software integration.
Translate complex engineering developments into practical adoption paths and actionable content for diverse audiences.
Identify product usability gaps and workflow improvements by capturing feedback from customers, partners, and developers.
Advocate for NVIDIA AI software within developer, cloud-native, and open-source ecosystems through technical storytelling.

What we're looking for

BS or MS in Computer Science, Engineering, AI/ML, Data Science, or a related technical field, or equivalent experience.
12+ years of proven experience in technical marketing engineering, software development, developer relations, solution architecture, technical writing, or product engineering.
Hands-on experience building, deploying, or explaining AI/ML, generative AI, RAG, agentic AI, LLM-based applications, 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 to translate sophisticated engineering details into useful content and excellent written, spoken, and visual communication skills.
Examples of published technical work such as documentation, blogs, tutorials, videos, conference talks, demos, GitHub projects, notebooks, or developer guides (preferred).
Experience with NVIDIA AI software or adjacent technologies such as NIM, NeMo, TensorRT, Triton Inference Server, RAPIDS, CUDA, or DGX Cloud (preferred).

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