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Nvidia Internships Jobs (NOW HIRING)

Nvidia

Senior Research Scientist, Rendering

Redmond, WA

$110K - $140K/yr

Collaborating with fantastic NVIDIA researchers, engineers, and external academics. * Helping define team research topics, goals, and guiding other researchers and interns. What we need to see:

Nvidia

Senior Robotics Research Scientist

Seattle, WA · On-site

$112K - $142K/yr

Collaborating with product managers and engineering teams to transfer your research into NVIDIA products that will have real-world impact; * Mentoring interns and more junior research scientists and ...

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How much do nvidia internships jobs pay per hour?

As of Aug 6, 2026, the average hourly pay for nvidia internships in the United States is $19.34, according to ZipRecruiter salary data. Most workers in this role earn between $15.38 and $21.63 per hour, depending on experience, location, and employer.

How can I get an internship in Nvidia?

To secure an internship at Nvidia, applicants should have strong technical skills in areas such as programming, computer architecture, or AI, and typically need to be enrolled in a relevant degree program. The application process involves submitting an online application through Nvidia's careers website, followed by interviews that assess technical knowledge and problem-solving abilities. Internships are competitive and often require prior project experience or coursework related to the role.

What is the difference between Nvidia Internships vs Nvidia Co-op Programs?

AspectNvidia InternshipsNvidia Co-op Programs
DurationTypically 10-12 weeks during summerUsually semester-long, 4-6 months
Work EnvironmentFull-time, project-basedPart-time, integrated with academic schedule
EligibilityUndergraduate or graduate studentsStudents enrolled in a co-op program or academic institution
Application ProcessOnline application, interviews, coding assessmentsApplication through university co-op office, interviews

Both Nvidia Internships and Nvidia Co-op Programs offer valuable industry experience, but internships are typically shorter, full-time summer roles, while co-op programs are longer, part-time roles integrated with academic schedules. Internships are ideal for students seeking quick industry exposure, whereas co-ops suit those combining work with ongoing studies.

What types of projects do Nvidia interns typically work on, and how much autonomy do they have during their internship?

Nvidia interns are often assigned to real-world projects that directly contribute to the company’s products, such as developing software tools, optimizing AI models, or supporting hardware design. Interns usually work within small, specialized teams and receive mentorship from experienced engineers and researchers. While guidance is provided, interns are encouraged to take initiative, propose solutions, and manage their own tasks, fostering both independence and professional growth. Regular check-ins and collaborative meetings help interns integrate with their teams and gain valuable industry experience.

What are the key skills and qualifications needed to thrive as an Nvidia intern?

To thrive as an Nvidia Intern, you generally need a strong academic background in computer science, engineering, or a related field, along with proficiency in programming languages like C++, Python, or CUDA. Familiarity with tools such as Git, Linux, and relevant development environments, as well as coursework or certifications in machine learning or graphics, is often expected. Initiative, teamwork, and effective communication are standout soft skills that help you learn quickly and contribute to projects. These abilities are crucial for making an immediate impact, adapting to challenging tasks, and collaborating within Nvidia's innovative and fast-paced environment.

What are Nvidia internships?

Nvidia internships are temporary positions offered to students and recent graduates, giving them the opportunity to work on real-world projects at Nvidia, a leading technology company known for its work in graphics processing units (GPUs), AI, and computing. Interns gain hands-on experience, mentorship from industry experts, and exposure to cutting-edge technology. These internships are typically available in areas such as engineering, software development, research, business, and marketing. Participants can work at Nvidia offices worldwide and may also benefit from networking events, workshops, and career development resources. Successful internships can often lead to full-time employment opportunities at Nvidia.

What degree is required for Nvidia internships?

Nvidia internships typically require applicants to be enrolled in a bachelor's or master's degree program related to computer science, engineering, or a similar field. Relevant skills, such as programming experience and familiarity with tools like CUDA or AI frameworks, are also important for eligibility.
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What states have the most Nvidia Internships jobs? States with the most job openings for Nvidia Internships jobs include:
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Nvidia Internships jobs near you
Infographic showing various Nvidia Internships job openings in the United States as of August 2026, with employment types broken down into 13% Internship, 1% As Needed, 68% Full Time, 16% Part Time, 1% Temporary, and 1% Summer. Highlights an 92% Physical, 4% Hybrid, and 4% Remote job distribution, with an average salary of $40,232 per year, or $19.3 per hour.

Senior Compiler Engineer, AI Inference Platforms

NVIDIA

Santa Clara, CA • On-site

$122K - $168K/yr

Full-time

Re-posted 13 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

Job Summary:
NVIDIA is known as 'the AI computing company' and is seeking a Senior Compiler Engineer for its Deep Learning & AI Compiler team. The role involves analyzing deep learning networks and developing compiler optimization algorithms to enhance NVIDIA's inference engine across various platforms.
Responsibilities:
• Analyzing deep learning networks and developing compiler optimization algorithms.
• Collaborating with members of the deep learning software framework teams and the GPU architecture teams to accelerate the next generation of deep learning software.
• Scope of these efforts includes defining public APIs, performance optimizations and analysis, crafting and implementing compiler techniques for AI workloads and future NVIDIA GPUs.
Qualifications:
Required:
• Bachelor’s, Master’s or Ph.D. in Computer Science, Computer Engineering, related field or equivalent experience.
• 3+ years of relevant work or research experience in performance analysis and compiler optimizations.
• Experience with compiler technologies (e.g., MLIR, LLVM, XLA, Triton, etc.).
• Excellent C/C++ and Python programming and software design skills, including debugging, performance analysis, and test design.
• Ability to work independently, define project goals and scope, and lead your own development efforts.
• Strong interpersonal skills are required along with the ability to work in a dynamic product-oriented team.
Preferred:
• Proficient in CPU and/or GPU architecture.
• CUDA or OpenCL programming experience.
• Understanding of deep learning models, algorithms and frameworks, such as PyTorch, JAX.
• GPU kernel authoring and performance analysis using tools such as Nsight Compute.
• A track record of success in mentoring early-career engineers and interns is a bonus.
• Track record on new hardware bring-up is a plus.
Company:
NVIDIA is a computing platform company operating at the intersection of graphics, HPC, and AI. Founded in 1993, the company is headquartered in Santa Clara, USA, with a team of 10001+ employees. The company is currently Late Stage.

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

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