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Internship Nvidia Autonomous Driving Jobs in Raleigh, NC

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than ... An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can ...

Senior Software Engineer, Agentic AI

Durham, NC · On-site

$118K - $156K/yr

An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can ... of autonomous systems? As a global leader in AI and deep learning, NVIDIA is redefining industries ...

NVIDIA is seeking best-in-class ASIC Design Engineers to design and implement the world's leading ... driving cars and the growing field of artificial intelligence. We have crafted a team of ...

NVIDIA is seeking outstanding ASIC Verification Engineer to verify the world's leading GPUs. This ... driving cars and the growing field of artificial intelligence. We have crafted a team of ...

Showing results 21-38

Internship Nvidia Autonomous Driving information

See Raleigh, NC salary details

$8

$15

$21

How much do internship nvidia autonomous driving jobs pay per hour?

As of Aug 8, 2026, the average hourly pay for internship nvidia autonomous driving in Raleigh, NC is $15.11, according to ZipRecruiter salary data. Most workers in this role earn between $12.16 and $17.07 per hour, depending on experience, location, and employer.

What is the difference between Internship Nvidia Autonomous Driving vs Intern Nvidia Computer Vision?

AspectInternship Nvidia Autonomous DrivingIntern Nvidia Computer Vision
Required CredentialsEnrolled in Computer Science, Electrical Engineering, or related fields; some knowledge of AI and roboticsEnrolled in Computer Science, Electrical Engineering, or related fields; strong programming skills in Python/C++
Work EnvironmentResearch labs, automotive industry projects, collaborative teamsResearch labs, AI development teams, tech industry settings
Employer & Industry UsageUsed by Nvidia in autonomous vehicle projects and automotive industryUsed by Nvidia in AI and computer vision applications across various sectors

Both internships involve AI, programming, and hardware knowledge, but the Autonomous Driving role focuses on vehicle systems and robotics, while Computer Vision emphasizes image processing and AI algorithms. Candidates should choose based on their interest in automotive applications versus general AI and vision tech.

What are popular job titles related to Internship Nvidia Autonomous Driving jobs in Raleigh, NC? For Internship Nvidia Autonomous Driving jobs in Raleigh, NC, the most frequently searched job titles are:
What job categories do people searching Internship Nvidia Autonomous Driving jobs in Raleigh, NC look for? The top searched job categories for Internship Nvidia Autonomous Driving jobs in Raleigh, NC are:
What cities near Raleigh, NC are hiring for Internship Nvidia Autonomous Driving jobs? Cities near Raleigh, NC with the most Internship Nvidia Autonomous Driving job openings:
Infographic showing various Internship Nvidia Autonomous Driving job openings in Raleigh, NC as of August 2026, with employment types broken down into 65% Full Time, 32% Part Time, and 3% Contract. Highlights an 96% Physical, 1% Hybrid, and 3% Remote job distribution, with an average salary of $31,430 per year, or $15.1 per hour.

Senior GPU Architect, Deep Learning

Nvidia

Durham, NC

Full-time

Re-posted 6 days ago


Nvidia rating

9.6

Company rating: 9.6 out of 10

Based on 17 frontline employees who took The Breakroom Quiz

8th of 242 rated software companies


Job description

We are now looking for a Senior GPU & Deep Learning Architect!

The NVIDIA GPU Architecture group is looking for world class architects and software developers to join and lead our various architecture efforts. A key part of NVIDIA's strength is to innovate in the graphics and parallel computing fields delivering the highest performance in the world for deep learning and parallel processing algorithms. We are constantly looking for ways to improve our GPU architecture, especially for deep learning workloads, both training and inference, and maintain our leadership by developing new parallel programming models, and new architectures required to make this successful. In this position, you will be responsible for developing and enhancing various features in the GPU architecture that advance the state of the art in parallel programming models or parallel computing performance. You would interact with other world-class architects and researchers to build simulators, mapping deep learning workloads to current and future hardware, and validate new architectural features.

What you'll be doing:

  • Design new hardware features for future processing architectures targeted at deep learning workloads, for both training and inference.

  • Advance the state of parallel computation.

  • Be knowledgeable about future parallel programming models and their impact to hardware.

  • Develop software for various hardware simulators, test infrastructures or metrics systems including databases.

  • Work in a team to document, design, develop tools to analyze and simulate, validate, and verify functional or performance models.

  • Develop tests, testplans, and testing infrastructure for new graphics or parallel processing architectures

  • Be hungry to learn and work on simulators, RTL and real silicon.

What we need to see:

  • MS in Computer Science, Electrical Engineering or Computer Engineering or equivalent experience.

  • Experience in working with hardware targeted at deep learning, or working on mapping deep learning algorithms to hardware.

  • 8+ years of relevant industry experience in GPU or other parallel programming architectures (or other equivalent experience).

  • Strong programming ability inC, C++, Perl andPython.

  • Background in computer architecture, parallel processing, signal processing and/or high performance computing.

  • Knowledge of state of the art in DL algorithms and attention mechanisms is a huge plus.

NVIDIA is widely considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and hard working people in the world working for us. If you're creative, autonomous, and love a challenge, consider joining our GPU Architecture team and help us build the real-time, cost-effective AI computing platform driving our success in this exciting and quickly growing field.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until January 13, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering a diverse 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.

What Nvidia employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom


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About Nvidia

Sourced by ZipRecruiter

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology--and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US

Year founded

1993