Performance Engineer Intern, Systems Software - Fall 2026

at Nvidia

USD 20-71 per hour

INTERN

✅ On-site

Tech Stack
Tag name is followed by "@" symbol and proficiency level value. About proficiency levels:

1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;

3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;

7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;

10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.

AI Algorithms @ 3 CUDA @ 3 Data Structures @ 3 Deep Learning GPU @ 3 HPC Kubernetes Linux @ 3 Machine Learning @ 3 PyTorch @ 2 Python @ 3 Slurm

Details

You would join the team responsible for the maintenance, development, and execution of Desktop Gaming Performance testing in Linux and Windows environments for the world's fastest, power efficient GPUs. This job has a preferred duration of 8-12 months.

Responsibilities

Writing and maintaining containerized GPU accelerated workloads for the financial services industry, from deep learning training and inference, to portfolio optimization and backtesting.
Running, validating, and analyzing benchmarking models at scale on HPC clusters.
Visualizing performance data, building charts and dashboards using internal schemas and tooling.
Working closely with the latest and greatest in financial AI models and tooling to help build reference models for NVIDIA.

Requirements

Enrolled in a Bachelors program majoring in Computer Engineering, Software Engineering, Computer Science, or related field.
Desire to improve code quality by learning and applying computer science fundamentals, algorithms, and data structures.
Comfort with teamwork, collaboration, and a desire to reach across functional borders to develop new partnerships.
Active experience with Python.
Working comfort in a Linux command-line environment with version control.
Foundational understanding and interest of the machine learning lifecycle (training, evaluation, and inference).

Ways to stand out from the crowd

Familiarity with PyTorch and/or training, testing, and evaluating machine learning models.
Experience with GPU computing or CUDA and libraries like cuOPT, CUTLASS, cuDNN, etc.
Exposure to workload orchestration and job schedulers (Kubernetes, Slurm).
Experience with containerized applications and resource management.
Interest in quantitative finance and applying performance data to real-world problems.

Our internship hourly rates are a standard pay based on the position, your location, year in school, degree, and experience. The hourly rate for our interns is 20 USD - 71 USD.

You will also be eligible for Intern benefits.

Applications for this job will be accepted at least until July 10, 2026.