Senior CUDA Driver & Kernel Scheduling Engineer - Equity

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

Job summary

NVIDIA is seeking a seasoned system software engineer to work on the CUDA Driver, delivering features and improvements to accelerate general purpose computation on the GPU. You will be part of a team that enables workloads across deep learning, scientific computing, data science, and gaming environments.

You will collaborate across teams to evangelize, architect, and implement new features, while driving improvements to CUDA APIs and programming models.

Qualifications

• BS or MS 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.
• Background with operating system interfaces for threads, process control, and virtual memory.
• Understanding of system level architecture, such as interconnects, memory hierarchy, interrupts, and memory-mapped IO.
• 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.
• Extend important CUDA programming models and functionality such as CUDA Graphs.
• Explore ways to use Graphs to improve the 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

Cross-team collaboration

OS concepts

Education

BS/MS in CS/EE or related

Job description

NVIDIA is seeking a seasoned system software engineer to work on the CUDA Driver, delivering features and improvements to accelerate general purpose computation on the GPU. You will be part of a team that enables workloads across deep learning, scientific computing, data science, and gaming environments.

You will collaborate across teams to evangelize, architect, and implement new features, while driving improvements to CUDA APIs and programming models.