Experience with GPU computing or CUDA and libraries like cuOPT, CUTLASS, cuDNN, etc. * Exposure to ... Our internship hourly rates are a standard pay based on the position, your location, year in school ...
Experience with GPU computing or CUDA and libraries like cuOPT, CUTLASS, cuDNN, etc. * Exposure to ... Our internship hourly rates are a standard pay based on the position, your location, year in school ...
Experience with GPU computing or CUDA and libraries like cuOPT, CUTLASS, cuDNN, etc. * Exposure to ... Our internship hourly rates are a standard pay based on the position, your location, year in school ...
Experience with GPU computing or CUDA and libraries like cuOPT, CUTLASS, cuDNN, etc. * Exposure to ... Our internship hourly rates are a standard pay based on the position, your location, year in school ...
Internship Cuda Programmer information
What is the difference between Internship Cuda Programmer vs Cuda Developer?
| Aspect | Internship Cuda Programmer | Cuda Developer |
|---|---|---|
| Credentials | Enrolled in or recent graduate of Computer Science or related field | Bachelor's or higher in Computer Science, with experience in CUDA programming |
| Work Environment | Internship setting, learning-focused, entry-level projects | Full-time professional role, developing complex GPU-accelerated applications |
| Industry Usage | Research labs, tech companies, internships for skill development | Tech firms, gaming, scientific computing, high-performance computing |
While an Internship Cuda Programmer is typically a learning position for students or recent graduates gaining foundational experience, a Cuda Developer is a full-time professional responsible for designing and optimizing GPU-accelerated software. The roles differ mainly in experience level, responsibilities, and career stage, but both require knowledge of CUDA programming and GPU architecture.
Full-time
Re-posted yesterday
Nvidia rating
9.6
Based on 17 frontline employees who took The Breakroom Quiz
8th of 242 rated software companies
Job description
NVIDIA is a worldwide technology company headquartered in Santa Clara, California. NVIDIA manufactures graphics processing units (GPUs), as well as system on a chip units (SOCs) for the expanding markets. Our work in visual computing, the art and science of computer graphics, has led to thousands of patented inventions, breakthrough technologies, deep industry relationships and a globally recognized brand. Our work opens up new universes to explore, enables amazing creativity and discovery, and powers inventions such as artificial intelligence and autonomous cars.
You would join the team responsible for the maintenance, development, and execution of Desktop Gaming Performance testing in Linux and Windows environments for the world's fastest, power efficient GPUs. This job has a preferred duration of 8-12 months.
What you'll be doing:
Writing and maintaining containerized GPU accelerated workloads for the financial services industry, from deep learning training and inference, to portfolio optimization and backtesting.
Running,validating, and analyzing benchmarking models at scale on HPC clusters.
Visualizing performance data, building charts and dashboards using internal schemas and tooling.
Working closely with the latest and greatest in financial AI models and tooling to help build reference models for NVIDIA.
What we need to see:
Enrolled in a Bachelors program majoring in Computer Engineering, Software Engineering, Computer Science, or related field.
Desire to improve code quality by learning and applying computer science fundamentals, algorithms, and data structures.
Comfort with teamwork, collaboration,and a desire to reach across functional borders to develop new partnerships.
Active experience with Python.
Working comfort in a Linux command-line environment with version control.
Foundational understanding and interest of the machine learning lifecycle (training, evaluation, and inference).
Ways to stand out from the crowd:
Familiarity with PyTorch and/or training, testing, and evaluating machine learning models.
Experience with GPU computing or CUDA and libraries like cuOPT, CUTLASS, cuDNN, etc.
Exposure to workload orchestration and job schedulers (Kubernetes, Slurm).
Experience with containerized applications and resource management.
Interest in quantitative finance and applying performance data to real-world problems.
You will also be eligible for Internbenefits.
Applications for this job will be accepted at least until July 10, 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.About Nvidia
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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