Description
As an Enterprise Platform TME intern at NVIDIA, you will help accelerate the adoption of NVIDIA Enterprise solutions by building workflows, demos, and technical content that showcase GPU-accelerated platforms.
You will stand up AI and agentic AI workflows end-to-end and explain them clearly to engineering audiences and at public events, developing 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 demos that showcase GPU-enabled enterprise platforms, produce technical collateral that supports them, and enable customers to develop AI locally on the systems they already trust.
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:
- 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
Nice to Have:
- 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
Benefits:
- Intern benefits
- Hourly rate: 20 USD - 71 USD