Deep Learning Performance Software Intern - 2027

AI in this role

rag

We are now looking for a Deep Learning Performance Software Engineering Intern!

We are expanding our research and development for deep learning. We seek excellent Software Engineers to join our team. We specialize in developing GPU-accelerated Deep learning software. Researchers around the world are using NVIDIA GPUs to power a revolution in deep learning, enabling breakthroughs in numerous areas. Join the team that builds software to enable new solutions. Your ability to work in a fast-paced customer-oriented team is required and excellent communication skills are necessary.

What you’ll be doing:

Creating and maintaining SKILL, Wiki, and agent harness
Develop TileGym, Triton CUDA TileIR backend and CUDA Tile
Develop highly optimized deep learning kernels through tile-based GPU programming model
End-to-end performance optimization through tile-based GPU programming model
Do performance optimization, analysis, and tuning

What we need to see:

Pursuing a degree from a university in an engineering or computer science related field. A masters or doctoral candidate is preferred.
Understands the core components of agentic systems, including LLM APIs, prompting, tool use/function calling, agent loops, planning, reasoning, memory, RAG, skills, MCP, sub-agents, multi-agent architectures, context engineering, and harness engineering.
Excellent C/C++ programming and software design skills
Python experience a plus
MLIR experience a plus
Performance modelling, profiling, debug, and code optimization or architectural knowledge of CPU and GPU
GPU programming experience (CUDA or OpenCL) desired

NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most brilliant and talented people on the planet working for us. If you're creative and autonomous, we want to hear from you!

How we rate this

Deep Learning Performance Software Intern - 2027 at NVIDIA rates 98 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

RAG

Questions you could be asked

How would you design a retrieval step so the model answers from real data instead of guessing?
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

List these exact terms on your resume: RAG. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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