AI Computing Software Development Intern - 2027

AI in this role

Develop and optimize AI inference pipelines and compiler backends for large language models and generative AI on GPUs.

pytorchpythontensorrtcudac

deep-learningcompiler-optimizationgpu-computingllm-inference

We are now looking for an AI Computing Software Development Intern!

NVIDIA invites skilled interns in artificial intelligence computing solutions to join our AI Compute team in Taiwan. This is your chance to work on one of the globe’s most advanced AI systems. You will help develop technologies for Large Language Models, Recommender Systems, and Generative AI, and push the limits of GPU performance for AI inference.

What you will be doing:

As an intern, you’ll focus on one of two specialized tracks: TensorRT-LLM – Inference Optimization (Python / PyTorch) or TensorRT Compiler – Graph Optimization (C++).

For TensorRT-LLM:

Build and enhance high‑performance LLM inference pipelines on key LLM models.
Analyze and optimize LLM model execution, scalability, and memory use.
Collaborate across framework and research teams to deliver efficient multi‑GPU model serving for Agentic AI workloads.

For TensorRT Compiler:

Work on the TensorRT compiler backend to improve graph transformations and code generation for NVIDIA GPUs.
Develop compiler optimization passes, refine operator fusion, and optimize memory usage.
Collaborate with CUDA and hardware architecture teams to accelerate Deep Learning inference computations.

What we need to see:

Pursuing a B.S., M.S., Ph.D., or equivalent in Computer Science, Computer Engineering, Electrical Engineering, Applied Mathematics, or related fields.
Excellent problem‑solving ability, curiosity for modern AI systems, and passion for GPU computing and deep learning software performance.
TensorRT‑LLM: Strong Python programming and experience with PyTorch; solid understanding of underlying LLM inference operations and GPU acceleration.
TensorRT Compiler: Proficient in C++, with experience in compiler or performance optimization.

Join us and play a part in building the AI computing platforms that drive innovation across industries worldwide.

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, sex, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform crucial job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.

How we rate this

AI Computing Software Development Intern - 2027 at NVIDIA rates 90 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

Deep LearningCompiler OptimizationGPU ComputingLLM InferencePyTorchPythonTensorrtCuda

Questions you could be asked

Tell me about a project where deep learning was part of your work. What did you do?
Tell me about a project where compiler optimization was part of your work. What did you do?
Tell me about a project where gpu computing was part of your work. What did you do?
Tell me about a project where llm inference was part of your work. What did you do?
Walk me through how you've used PyTorch in your day-to-day work.

Adapt your resume

List these exact terms on your resume: Deep Learning, Compiler Optimization, GPU Computing, LLM Inference, and PyTorch. 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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