Senior CUDA Driver & Systems Software Engineer

NVIDIA Corporation

Santa Clara (CA)

On-site

USD 184,000 - 288,000

Full time

14 days+
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Job summary

NVIDIA is seeking a seasoned system software engineer to work on the CUDA Driver, a core component of our platform for accelerating general purpose computation on the GPU. You will be integral to a team delivering features and improvements for diverse workloads including AI, scientific computing, data science, and gaming.

You will evangelize and architect new features, coordinate across teams, and refine the CUDA APIs and programming model.

Qualifications

  • BS or MS degree in Computer Science, Electrical Engineering or related field (or equivalent experience).
  • Strong C and C++ programming skills.
  • Minimum of 7 years of related development experience.
  • Experience driving projects across multiple teams.
  • Experience working with large codebases.
  • Background with operating system interfaces for threads, process control, and virtual memory.
  • Experience writing and debugging multithreaded programs.
  • Good written communication as well as presentation skills.

Responsibilities

  • Evangelize, architect, and implement new features.
  • Coordinate and drive development efforts across multiple teams.
  • Help define forward-looking improvements to the CUDA APIs and programming model.
  • Write effective, maintainable, and well-tested code.
  • Develop code for multiple operating systems.

Skills

C programming
C++ programming
Multithreading
OS concepts
Cross-team collaboration

Education

BS or MS in CS/EE or equivalent

Tools

CUDA Driver
Linux
Windows

Job description

NVIDIA is seeking a seasoned system software engineer to work on the CUDA Driver, a core component of our platform for accelerating general purpose computation on the GPU. You will be integral to a team delivering features and improvements for diverse workloads including AI, scientific computing, data science, and gaming.

You will evangelize and architect new features, coordinate across teams, and refine the CUDA APIs and programming model.

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