Performance Engineering Intern
NVIDIA
This job at a glance
- Who can apply
- New grads and up to 1 year
- Must know
- Embedded systemsPython
- Work
- On-site in Santa Clara · internship
- Pay
- Not stated by the employer.
- Skill demand
- Python21%Embedded systems3%of 99 similar entry-level jobs posted in the US this week
- Listed
- Nov 11, 2026 · still open
Sign in free to see which of these skills your resume already shows.
About the Role
Internship focused on post-silicon performance and power benchmarking on NVIDIA and competitive GPU products across notebook, desktop, workstation, and embedded platforms. This role involves using industry-standard tools and applications, providing compiled and analyzed data to internal teams, and utilizing Python scripting for automation and tool development.
Preferred internship duration: 8 months minimum
Key Responsibilities
- Implement hands-on tests focusing on performance and power on current GPU platforms across real-world applications from gaming, professional visualization, and AI.
- Set up and maintain test systems by configuring hardware, operating systems, drivers, and software tools used for benchmarking and data collection.
- Analyze benchmark and power data, turning raw results into clear reports to inform hardware, software, and product teams on GPU efficiency and performance.
- Debug issues related to applications, scripts, or hardware during testing and collaborate with engineering teams for resolution.
- Contribute data and insights that influence future GPU builds and enable comparison of NVIDIA products against competing solutions.
- Develop, implement, and maintain automation tools to improve testing efficiency.
Requirements & Tech Stack
- Pursuing a BS or MS in Computer Engineering, Electrical Engineering, Computer Science, or a related field.
- Hands-on programming experience with Python, including writing scripts for automation, data processing, or testing.
- Academic or project experience with computer architecture fundamentals.
- Experience with lab equipment such as DMMs, Agilent DAQs, or National Instruments DAQs.
- Exposure to performance or power measurement, including collecting, comparing, and interpreting results from experiments or coursework.
Ways To Stand Out
- Interest in GPU technology and the interplay between hardware and software for performance per watt improvement.
- Familiarity with electrical fundamentals or lab equipment used for power measurement (e.g., multimeters, data acquisition tools).
- Experience running application-based benchmarks across Gaming, ProViz, AI domains.
- Passion for learning, collaboration, and contributing to real product decisions.