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NVIDIA

Designs the chips and software that power AI

Developer and Performance Technology Intern

Hardware Validation & TestingConfirmed live in the last 24 hours
Internship · Winter 2027$20 - $71 /hrIn-person

About the Job

Requirements

  • Must be actively enrolled in a university pursuing a B.S. or M.S. degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field for the full 8–12-month internship.
  • The anticipated graduation date, including month and year, must be clearly indicated on the resume or CV.
  • Depending on the internship role, prior experience or knowledge could be required in GPU-accelerated deep learning frameworks such as TensorFlow, PyTorch, MXNet, TensorRT, Torch, and DML; GPU programming with CUDA or OpenCL; HPC applications such as LAMMPS, GROMACS, Amber, and RTM; Linux; Python and Unix shell scripting; containers such as Docker or Singularity; 3D graphics; image quality and power testing; low-level system configuration including BIOS configurations, memory timing, cache-latency control, and overclocking; electrical fundamentals including power measurement and multimeters or data acquisition tools; compilers such as GNU, Intel Composer, or PGI; clusters such as Slurm and Kubernetes; embedded platforms; debugging; benchmarking; and power testing.

Responsibilities

  • Run performance, image-quality, and power tests for Professional Visualization, AI, and LLM benchmark applications on various GPUs, and configure computer systems with appropriate hardware and software to run benchmarks.
  • Build automation scripts for benchmarking procedures and balancing configuration files; assemble computer hardware; develop and run automation scripts on applications; and design tools.
  • Complete post-silicon performance and power benchmarking on NVIDIA and competitive GPU products, and compile and analyze data for internal software, hardware, sales, and marketing groups to inform decisions.
  • Develop, implement, and maintain test systems by configuring hardware, operating systems, drivers, and software tools used for benchmarking and data collection; implement hands-on tests focused on performance and power for GPU platforms; and maintain automation tools to improve testing efficiency.
  • Plan and execute GPU performance benchmarking across a wide range of HPC and deep-learning frameworks and applications; aggregate, analyze, and generate written and visual reports with testing data for internal teams.
  • Write scripts to improve data gathering through automation, design efficient processes for testing a wide variety of applications and hardware, and assist with developing tools and processes to improve the performance of automated testing.

About the Company

NVIDIA

Designs the chips and software that power AI

NVIDIA designs the chips and software that power artificial intelligence and sells complete data center systems to cloud providers, AI labs, and other businesses, along with GeForce graphics cards for gamers. Its GPUs, chips that perform thousands of calculations at once, became the main hardware for AI after researchers used them to train the AlexNet image-recognition model in 2012. NVIDIA connects many GPUs with its own processors and networking gear into servers and rack-sized computers, and developers program them through its CUDA software. These systems bring in most of its sales, and it also makes chips for cars and robots. Jensen Huang co-founded NVIDIA in 1993, and it is based in Santa Clara, California.

Company size
10,001+
Company stage
IPO
Headquarters
Santa Clara, CA
Founded
1993

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