Principal Engineer, CUDA UMD - GPU Kernel Scheduling

NVIDIA•Santa Clara, CA

3d•$272,000 - $431,250

About The Position

NVIDIA is seeking a motivated system software engineer with deep understanding of device drivers and phenomenal C/C++ skills to work on the CUDA Driver. This role is integral to a team that delivers features and improvements to realize the potential of NVIDIA hardware for various computational workloads, including deep learning, scientific computation, data science, self-driving cars, video games, and virtual reality. The engineer will use design abilities, coding expertise, and creativity to craft solutions, shape the future direction of CUDA, and collaborate with peers across NVIDIA.

Requirements

BS or MS degree in Computer Science, Electrical Engineering or related field (or equivalent experience)
Strong C and C++ programming skills
Minimum of 15+ 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

Nice To Haves

Prior experience with parallel computing - preferably writing CUDA Programs or Libraries that use CUDA
Understanding of system level architecture, such as interconnects, memory hierarchy, interrupts, and memory-mapped IO
Knowledge of memory coherence and consistency models
Background with kernel mode development
Experience with Linux Systems Software development as well as experience maintaining and extending programming models or higher-level language support for similar environments

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