Developer Technology Engineering Intern - Compute Performance

NVIDIA; Zürich; Vor Ort; CHF 90.000 - 120.000; Vollzeit

Zusammenfassung

NVIDIA is seeking MSc/PhD students to research and develop GPU-accelerated techniques for high-performance computing, spanning scientific computing, computational engineering, and data science.

You will optimize performance across current and future GPU architectures and collaborate with architecture, research, libraries, and tools teams to shape next-generation platforms and programming models.

Qualifikationen

• Pursuing a MSc or preferably PhD degree in an engineering or computer science related discipline.
• Domain expertise in telecommunications, medical imaging, machine learning, deep learning, or natural sciences is helpful.

Aufgaben

• Research and develop techniques to GPU-accelerate leading applications in HPC fields.
• Perform in-depth analysis and optimization for best performance on current and next-generation GPU architectures.
• Collaborate with architecture, research, libraries, tools, and system software teams to influence design and programming models.

Kenntnisse

C/C++

Fortran

Parallel programming

CUDA/OpenACC

Mathematics

Communication

Ausbildung

MSc or PhD in CS/Engineering

Tools

CUDA

OpenACC

Jobbeschreibung

NVIDIA is hiring passionate, world-class computer science studetns to work in its Compute Developer Technology (Devtech) team. With highly competitive salaries and a comprehensive benefits package, NVIDIA is widely considered to be one of the technology industry's most desirable employers. We have some of the most brilliant and talented people in the world working with us and our engineering teams are growing fast in some of the hottest and state of the art fields: Deep Learning, Artificial Intelligence, Autonomous Vehicles, Supercomputing and more. Are you a creative and autonomous computer scientist with a real passion for parallel computing? If so, we want to hear from you.

What you will be doing:

In this role, you will research and develop techniques to GPU-accelerate leading applications in high performance computing fields within scientific computing, computational engineering, and data science. You will be performing in-depth analysis and optimization to ensure the best possible performance on current and next-generation GPU architectures. This involves:

• Working directly with key application developers to understand the current and future problems they are solving, crafting and optimizing core parallel algorithms and data structures to provide the best solutions using GPUs, through both library development and direct contribution to the applications.
• Collaborating closely with diverse groups at NVIDIA such as the architecture, research, libraries, tools, and system software teams to influence the design of next-generation architectures, software platforms, and programming models, by investigating the impact on application performance and developer productivity.

What we need to see:

• Pursuing a MSc or preferably PhD degree in an engineering or computer science related discipline. While not a requirement, domain expertise in telecommunications, medical imaging, machine learning, deep learning, or natural sciences is helpful.
• Programming fluency in C/C++ and/or Fortran with a deep 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 and organization skills, with a logical approach to problem solving, good time management, and task prioritization skills.

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.