GPU Compute DevTech Intern: HPC & Parallelization

NVIDIA; West of England; On-site; GBP 42,000 - 54,000; Full time

Benefits offered by this job

Competitive salary

Benefits package

Job summary

NVIDIA is hiring highly talented computer science students to join the Compute Developer Technology team and explore GPU-accelerated computing across deep learning, AI, and HPC. You will work on developing techniques to accelerate applications on modern GPU architectures with a focus on performance and scalability.

The role emphasizes collaboration with architecture, libraries, and tool teams and requires strong mathematical and programming foundations.

Qualifications

• Pursuing MSc or PhD in engineering or CS; domain knowledge helpful.
• Fluency in C/C++ and/or Fortran with solid design and algorithms.
• Strong mathematical background in linear algebra and numerical methods.
• Experience with parallel programming, ideally CUDA C/C++ and OpenACC.
• Strong communication, organization, time management and problem-solving skills.

Responsibilities

• Research and develop techniques to GPU-accelerate leading applications in HPC.
• Perform in-depth analysis and optimization for current and future GPU architectures.

Skills

Parallel programming

C/C++ programming

Fortran programming

Communication skills

Time management

Education

MSc in engineering or computer science

PhD (preferred)

Tools

CUDA C/C++

OpenACC

OpenMP

Job description

NVIDIA is hiring highly talented computer science students to join the Compute Developer Technology team and explore GPU-accelerated computing across deep learning, AI, and HPC. You will work on developing techniques to accelerate applications on modern GPU architectures with a focus on performance and scalability.

The role emphasizes collaboration with architecture, libraries, and tool teams and requires strong mathematical and programming foundations.