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NVIDIA pioneered accelerated computing to tackle challenges no one else can solve. Our work in AI and digital twins is transforming the world's largest industries and profoundly impacting society — from gaming to robotics, self-driving cars to life-saving healthcare, climate change to virtual worlds where we can all connect and create. Our internships offer an excellent opportunity to expand your career and get hands on experience with one of our industry-leading Deep Learning teams.
What you'd do
- Running performance, image quality, and power tests for Professional Visualization, AI, and LLM benchmark applications on various GPUs; Configuring computer systems with appropriate hardware and software to run benchmarks
- Building automation scripts to benchmarking procedure and balancing configuration files; assembling computer hardware, developing and running automation scripts on applications, and designing tools
- Course or internship experience related to the following areas could be required: Linux and Shell Scripting, GPU Accelerated Deep Learning Frameworks (TRT, Torch, DML), Python, Containers (Docker or Singularity), Embedded Platforms, 3D Graphics, GPU Programming (CUDA, OpenCL), Benchmarking, Image Quality and Power Testing, Scripting, Debugging, Low Level System Configuration (BIOS Configurations, Memory Timing, Controlling Cache Latency, Overclocking)
- Completing post-silicon performance and power benchmarking on NVIDIA and competitive GPU products; Compiling and analyzing data for internal software, hardware, sales, and marketing groups to inform decisions
- Developing, implementing, and maintaining test systems by configuring hardware, operating systems, drivers, and software tools used for benchmarking and data collection; Implementing hands-on tests focused on performance and power for GPU platforms; Maintaining automation tools to improve testing efficiency
- Course or internship experience related to the following areas could be required: Linux, Python Scripting, Debugging, Electrical Fundamentals (Power Measurement; Multimeters or Data Acquisition Tools), MMs, Agilent DAQs, National Instruments DAQs, GPU Programming (CUDA, OpenCL), Embedded Platforms, Benchmarking, Power Testing
- Planning and executing GPU performance benchmarking across a wide range of HPC and DL Frameworks and Applications; Aggregating, analyzing, and generating written and visual reports with testing data for internal teams
- Writing scripts to improve data gathering through automation, designing efficient processes for testing a wide variety of applications and hardware; Assisting with the development of tools and processes to improve performance of automated testing
- Course or internship experience related to the following areas could be required: GPU-Enabled Deep Learning Frameworks (TensorFlow, PyTorch, MXNet, TensorRT), GPU-Enabled HPC Applications (LAMMPS, GROMACS, Amber, RTM), GPU/CPU Benchmarking (Coud Solutions i.e.
- AWS, GCP, Azure), GPU Programming (CUDA, OpenACC, OpenCL), Compilers (GNU, Intel Composer, PGI), Clusters (Slurm, Kubernetes)
- 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 duration of the internship; anticipated graduation date (month and year) must be clearly indicated on a resume or CV to be considered.
- Starts
- 2026-08-19