NVIDIA Spring 2027 Internships: Developer and Performance Technology

NVIDIA•Santa Clara, CA

9d•$20 - $71

About The Position

NVIDIA is seeking students for its Spring 2027 Developer and Performance Technology internship opportunities. During the full-time, 8–12-month internship, students will work on projects with measurable business impact and gain hands-on experience with NVIDIA teams. Potential areas include Performance Engineering, Platform Performance and Power, and Deep Learning and High-Performance Computing (HPC). The internship requires continued enrollment in a relevant B.S. or M.S. program for its full duration. Preferred start dates are February 2027 or May 2027. Applications are reviewed on an ongoing basis. Performance Engineering interns may run performance, image-quality, and power tests for Professional Visualization, AI, and LLM benchmark applications on GPUs; configure systems for benchmarking; assemble hardware; build automation scripts and tools; and develop or balance configuration files. Platform Performance and Power interns may benchmark post-silicon performance and power on NVIDIA and competing GPU products, analyze data for internal groups, configure and maintain test systems, perform hands-on GPU platform tests, and maintain automation tools. Deep Learning and HPC interns may plan and execute GPU benchmarks across HPC and deep-learning frameworks and applications, analyze and report test data, automate data gathering, design testing processes, and help improve automated-testing tools and processes. NVIDIA states that specific experience or knowledge may be required depending on the internship role. The expected hourly rate is $20–$71. Intern benefits are available. Applications are accepted on an ongoing basis, and the posting is for an existing vacancy.

Requirements

Must be actively enrolled in a university and pursuing a B.S. or M.S. degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field throughout the full 8–12-month internship.
The anticipated graduation month and year must be clearly indicated on the resume or CV.
Depending on the role, relevant prior experience or knowledge may include GPU-accelerated deep-learning frameworks such as TensorFlow, PyTorch, MXNet, TensorRT, Torch, or DML; GPU programming with CUDA or OpenCL; HPC applications such as LAMMPS, GROMACS, Amber, or RTM; Linux; Python and Unix shell scripting; and Docker or Singularity containers.
Depending on the role, relevant prior experience or knowledge may include 3D graphics, image-quality and power testing, low-level system configuration, electrical fundamentals and power measurement, compilers, clusters, embedded platforms, debugging, and benchmarking.

Responsibilities

Run performance, image-quality, and power tests for Professional Visualization, AI, and LLM benchmark applications on GPUs.
Configure computer systems with suitable hardware and software for benchmark runs; assemble hardware and develop automation scripts, configuration files, and tools.
Complete post-silicon performance and power benchmarking on NVIDIA and competing GPU products.
Compile and analyze benchmark data for internal software, hardware, sales, and marketing groups.
Develop, implement, and maintain test systems by configuring hardware, operating systems, drivers, and benchmarking and data-collection software.
Perform hands-on performance and power tests for GPU platforms and maintain automation tools to improve testing efficiency.
Plan and execute GPU performance benchmarking across HPC and deep-learning frameworks and applications.
Aggregate and analyze testing data and create written and visual reports for internal teams.
Automate data gathering, design efficient testing processes, and assist with improving tools and processes for automated testing.