NVIDIA 2027 Internships: Deep Learning Computer Architecture
At a glance
Mid-level AI Research role at NVIDIA. US, CA, Santa Clara · full-time.
Pay not stated AI Research salary
aimlroles summary, based on the employer's posting.
What you'll do
- Take on internship projects designed to produce a measurable effect on the work
- Contribute to deep learning computer architecture work with one of the internship teams
- Apply your skills to work in a relevant focus area, such as GPU architecture or neural networks
What you bring
- Stay enrolled through the internship in a bachelor's, master's or Ph.D. program in electrical engineering, computer engineering or a related field
- Show your expected graduation month and year on your resume or CV
- Bring coursework or internship experience in GPU architecture, deep learning or another listed focus area
Who this fits
This is a full-time, twelve-week internship for students pursuing relevant degrees. Depending on the specific role, you may also need experience with programming languages, computing technologies or deep learning frameworks.
From the employer
By submitting your resume, you acknowledge that your 2027 Deep Learning Computer Architecture internship application will be processed in accordance with NVIDIA’s Applicant Privacy Policy and you agree to our Terms of Service. We’ll review resumes on an ongoing basis, and a recruiter may reach out if your experience fits one of our many internship opportunities.
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 Computer Architecture teams. We’re seeking strategic, ambitious, hard-working, and creative individuals who are passionate about helping us tackle challenges no one else can solve.
Throughout the 12-week full-time internship, students will work on projects that have a measurable impact on our business. We’re looking for students pursuing a B.S., M.S., or Ph.D. degree within a relevant or related field.
What we need to see:
- Must be actively enrolled in a university pursuing a B.S., M.S., or Ph.D. degree in Electrical Engineering, Computer Engineering, or a related field, for the full duration of the internship; anticipated graduation date (month and year) must be clearly indicated on a resume or CV to be considered.
Course or internship experience related to the following areas could be required:
- Experience in one or more of these Focus Areas: GPU Architecture, CPU Architecture, Deep Learning, GPU Computing, Parallel Programming, or High-Performance Computing Systems
- Modelling/Performance Analysis, Parallel Processing, Neural Network Architectures, GPU Acceleration, Deep Learning Neural Networks, Compiler Programming
- Performance Modeling, Profiling, Optimizing, and/or Analysis
- Depending on the internship role, prior experience or knowledge requirements could include the following programming skills and technologies: C, C++, Perl, CUDA, GPU Computing (CUDA, OpenCL, OpenACC), Deep Learning Frameworks (PyTorch, TensorFlow, Caffe), HPC (MPI, OpenMP)
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Our internship hourly rates are a standard pay based on the position, your location, year in school, degree, and experience. The hourly rate for our interns is 20 USD - 71 USD.You will also be eligible for Intern benefits.
Applications are accepted on an ongoing basis.This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.