7 days ago
Zürich, SwitzerlandIntern
Responsibilities
- Research and develop techniques to GPU-accelerate applications in scientific computing, computational engineering, and data science.
- Analyze and optimize application performance for current and next-generation GPU architectures.
- Work with application developers to design and optimize parallel algorithms and data structures using GPUs.
- Develop libraries and contribute directly to leading applications.
- Collaborate with architecture, research, libraries, tools, and system software teams on future architectures, software platforms, and programming models.
Requirements
- Pursuing an MSc or preferably PhD in computer science or an engineering-related discipline.
- Programming fluency in C/C++ and/or Fortran, with a strong understanding of software design, programming techniques, and algorithms.
- Strong mathematical fundamentals, including linear algebra and numerical methods.
- Experience with parallel programming, ideally CUDA C/C++ and OpenACC.
- Strong communication, organization, problem-solving, time-management, and task-prioritization skills.
- Domain expertise in telecommunications, medical imaging, machine learning, deep learning, or natural sciences is helpful but not required.
Benefits
- Competitive salary and comprehensive benefits package.
- NVIDIA is committed to a diverse and equal-opportunity work environment.
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.