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Internship Gpu Programming Jobs (NOW HIRING)

Nvidia

Senior Research Engineer - Enterprise Products

$107K - $146K/yr

A history of mentoring junior engineers and interns is a huge plus. * A desire to constantly grow ... GPU programming (CUDA). Your base salary will be determined based on your location, experience, and ...

Nvidia

Senior GPU Memory Architect

Austin, TX · On-site

This GPU memory architecture team creates new, innovative products tailored to NVIDIA's world ... Your history of successfully mentoring junior engineers and interns is a huge plus. Ways to stand ...

Nvidia

Senior GPU Memory Architect

Santa Clara, CA · On-site

This GPU memory architecture team creates new, innovative products tailored to NVIDIA's world ... Your history of successfully mentoring junior engineers and interns is a huge plus. Ways to stand ...

Nvidia

Senior GPU Memory Architect

Durham, NC · On-site

This GPU memory architecture team creates new, innovative products tailored to NVIDIA's world ... Your history of successfully mentoring junior engineers and interns is a huge plus. Ways to stand ...

Intel

AI Software Engineering Intern

Phoenix, AZ · On-site

... GPU programming, and performance optimization. Contributes to the design, development, and ... Available to work part-time for 6-12 months and to work onsite for the duration of the internship ...

Intel

AI Software Engineering Intern

Hillsboro, OR · On-site

... GPU programming, and performance optimization. Contributes to the design, development, and ... Available to work part-time for 6-12 months and to work onsite for the duration of the internship ...

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How much do internship gpu programming jobs pay per hour?

As of Aug 21, 2026, the average hourly pay for internship gpu programming in the United States is $25.42, according to ZipRecruiter salary data. Most workers in this role earn between $20.67 and $28.85 per hour, depending on experience, location, and employer.

What is an internship in GPU programming?

An Internship in GPU Programming is a temporary position, often held by students or recent graduates, where individuals gain hands-on experience working with Graphics Processing Units (GPUs) to develop, optimize, and accelerate software applications. Interns typically work on projects involving parallel computing, machine learning, graphics rendering, or scientific simulations using programming languages such as CUDA or OpenCL. These internships provide an opportunity to learn from experienced engineers, contribute to real-world projects, and develop specialized skills that are valuable in technology and research industries.

What types of projects or tasks can an intern expect to work on in a GPU programming internship?

As a GPU programming intern, you can expect to work on tasks such as optimizing existing code for GPU acceleration, developing parallel algorithms using CUDA or OpenCL, and assisting in the profiling and debugging of GPU applications. Interns often collaborate with researchers and software engineers to implement new features or improve the performance of computational workflows. You may also contribute to documentation and testing, gaining exposure to real-world applications in fields like machine learning, scientific computing, or graphics rendering.

What are the key skills and qualifications needed to thrive as an internship in GPU programming, and why are they important?

To thrive as an Internship GPU Programming, you need a solid background in computer science, mathematics, and programming languages such as C++ and Python, often supported by coursework or personal projects in parallel computing. Familiarity with GPU programming frameworks like CUDA or OpenCL and version control systems (e.g., Git) is typically expected. Strong analytical thinking, attention to detail, and effective communication help interns collaborate with teams and troubleshoot complex issues. These skills and qualities are essential for efficiently developing, optimizing, and debugging GPU-accelerated applications in a fast-paced, technical environment.
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Infographic showing various Internship Gpu Programming job openings in the United States as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% Remote job distribution, with an average salary of $52,867 per year, or $25.4 per hour.

Senior Research Engineer - Enterprise Products

Nvidia

Remote

$107K - $146K/yr

Full-time

Re-posted yesterday

Nvidia rating

9.6

Company rating: 9.6 out of 10

Based on 17 frontline employees who took The Breakroom Quiz

7th of 245 rated software companies

Job description

We are now looking for a Senior Research Engineer passionate about Generative AI inference. Are you excited to change the way people infuse AI into products and services? NVIDIA is at the forefront of generative AI models, from language to images. NVIDIA provides building blocks to democratize AI and make generative AI easy to develop, integrate, and deploy. Our team is dedicated to developing optimized inferencing technologies to support our growing generative AI needs. We contribute to all steps of the machine learning lifecycle: from conceptualization, to applied research, engineering for optimized inference, and deployment. Collaborate with research teams, engineers, and open-source community.

What you will be doing:

  • Design and evaluate routing policies for LLM traffic to best use mixture of model systems.

  • Build and run agentic benchmarks (e.g., Terminal-Bench ) to measure algorithm quality, and turn results into calibration data and routing profiles

  • Ship to an open-source repo: design docs, code review, docs, and community contributions

  • Collaborating with engineering teams across all of NVIDIA to ensure our software integrates seamlessly up and down the NVIDIA accelerated serving stack.

What we need to see:

  • Bachelor's of Master's degree in Computer Science or equivalent experience.

  • 8+ years of industry experience in Deep Learning frameworks (PyTorch or TensorFlow).

  • Experience designing or running LLM evaluations/benchmarks - ideally agentic ones - and drawing statistically sound conclusions from them

  • Understanding of modern techniques in Machine Learning, Deep Neural Networks, Natural Language Processing, or Speech Recognition.

  • Empirical research mindset: forming hypotheses about new algorithms, running calibrations, iterating on results

  • Strong communication and interpersonal skills, along with the ability to work in a dynamic and distributed team. A history of mentoring junior engineers and interns is a huge plus.

  • A desire to constantly grow and learn new things.

  • Strong computer science fundamentals - algorithms and data structures, computational complexity, parallel and distributed computing, system software.

Ways to stand out from a crowd:

  • Experience architecting or developing large-scale distributed systems for deep learning.

  • Agentic benchmark creation and publications.

  • Knowledge of CPU and/or GPU architecture.

  • GPU programming (CUDA).

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 192,000 USD - 304,750 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 July 14, 2026.

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.

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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