About the role#
NVIDIA is looking for students to join our Deep Learning Computer Architecture teams for a 12-week full-time internship. You will work on projects that have a measurable impact on the business. We review applications on an ongoing basis throughout the year.
What you'll do#
- Contribute to projects that directly affect our business operations.
- Gain hands-on experience working with teams focused on deep learning and computer architecture.
What you'll need#
- Current enrollment in a B.S., M.S., or Ph.D. program in Electrical Engineering, Computer Engineering, or a related field.
- Your resume or CV must clearly state your anticipated graduation date.
- Experience in areas such as GPU or CPU architecture, deep learning, or GPU computing.
- Background in parallel programming, high-performance computing systems modeling, performance analysis, or neural network architectures.
- Experience with GPU acceleration, compiler programming, performance modeling, profiling, or optimization.
Location & details#
- This position is located in Santa Clara, California.
- The role is on-site.
- This is a paid, full-time internship.
About NVIDIA
NVIDIA operates as a computer hardware manufacturer based in Santa Clara, California. Founded in 1993, the company focuses on accelerated computing and graphics technology. It produces hardware for markets including artificial intelligence, gaming, and data centers. The organization employs over 50,000 people and maintains a global presence.
How to get in at NVIDIA
Applying early gives you a distinct advantage at NVIDIA because recruiters review applications as they arrive. Intern Insider sends an instant alert the moment a role matching your target is published, so you can apply among the first before the pile grows. Getting your resume in front of a human early is often the difference between a screening call and a rejection. You can use Intern Insider to surface the recruiters behind NVIDIA roles to reach out directly. Asking a recruiter about the role or a referral materially improves your response rates compared to submitting into a general queue. It is a simple way to make your application stand out in a competitive process.
