Senior CUDA Driver & Kernel Scheduling Engineer (Equity)

NVIDIA Gruppe; Santa Clara (CA); On-site; USD 152,000 - 288,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA is seeking a motivated 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 contribute to features and improvements spanning deep learning, scientific computation, data science and autonomous systems.

You will evangelize, architect, and implement new capabilities, coordinate across teams, and help shape future CUDA APIs and programming models to unlock the full potential of NVIDIA hardware.

Qualifications

• BS or MS degree in Computer Science, Electrical Engineering or related field (or equivalent experience).
• Strong C and C++ programming skills.
• Minimum of 4 years of related development experience.
• Experience driving projects across multiple teams.
• Experience working with large codebases.

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.
• Extend important CUDA programming models and functionality such as CUDA Graphs.
• Explore ways to use Graphs to improve scheduling of AI/ML workloads on our GPUs to be more efficient and faster.
• Write effective, maintainable, and well-tested code.
• Develop code for multiple operating systems.

Skills

C/C++ programming

Multithreaded programming

Large codebases

Operating system interfaces

Cross-team collaboration

Written communication

Education

BS or MS in Computer Science or Electrical Engineering or related field

Tools

CUDA

Linux development

Job description

NVIDIA is seeking a motivated 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 contribute to features and improvements spanning deep learning, scientific computation, data science and autonomous systems.

You will evangelize, architect, and implement new capabilities, coordinate across teams, and help shape future CUDA APIs and programming models to unlock the full potential of NVIDIA hardware.