Enterprise Platform Technical Marketing Engineer Intern

About the Role

As an Enterprise Platform TME intern, you will help accelerate adoption of NVIDIA Enterprise solutions by building workflows, demos, and technical content that show partners and customers what GPU-accelerated platforms make possible. You will stand up AI and agentic AI workflows end-to-end and then explain them clearly to engineering audiences and at public events, developing the technical marketing assets that bring them to life — product and technology demonstrations, videos, playbooks, and targeted content. A core part of the role is translating Enterprise Platform capabilities into real-world use cases across Manufacturing, AECO, and Media & Entertainment. Working closely with key OEM partners and internal teams, you will help define the demos that showcase GPU-enabled enterprise platforms, produce the technical collateral that supports them, and enable customers to develop AI locally on the systems they already trust.

Position includes owning a summer project: a "hero demo" showcasing an end-to-end GPU-accelerated workflow; Pitch the demo concept directly, driving ownership of the idea from the start; A follow-on project may be assigned if the hero demo is completed early and to a high standard.

Key Responsibilities

• Building AI and agentic AI workflows end-to-end on NVIDIA platforms, including hardware bring-up and troubleshooting
• Creating technical marketing assets such as product demonstrations, videos, playbooks, and one-pagers
• Translating enterprise platform capabilities into real-world use cases across manufacturing, architecture and construction, and media and entertainment
• Defining and testing demos alongside original equipment manufacturer (OEM) partners and internal teams, and organizing a shared demo library
• Owning a summer "hero demo" project: an end-to-end GPU-accelerated workflow that you pitch and then build

Requirements & Tech Stack

• Pursuing a BS or MS in Computer Science, Computer Engineering, or a related technical field (graduating December 2027 or later)
• Programming experience in Python
• Working knowledge of Linux and Windows, including command-line tooling and system configuration
• Hands-on experience with an AI or machine learning framework such as PyTorch, TensorFlow, or Hugging Face
• Experience documenting a technical workflow and presenting it to a technical audience

Qualifications (Ways To Stand Out)

• Experience with large language models, retrieval-augmented generation (RAG) pipelines, or agentic AI frameworks such as LangChain, LlamaIndex, or NVIDIA NIM
• Familiarity with CUDA, GPU acceleration concepts, or NVIDIA developer software development kits such as TensorRT, RAPIDS, or Omniverse
• Working knowledge of containers and environment management (Docker, Conda, WSL), Git, and C/C++ or shell scripting
• Hands-on PC or workstation hardware experience, including component installation, system bring-up, driver troubleshooting, and benchmarking
• Projects you can demo: a GitHub portfolio, hackathon builds, research prototypes, technical blogs, or demo videos