Senior Software Engineer CUDA UMD - GPU Kernel Scheduling

NVIDIA•Santa Clara, CA

8d•$152,000 - $287,500

About The Position

NVIDIA is seeking a seasoned system software engineer to work on the CUDA Driver, a core component of its platform for accelerating general-purpose computation on GPUs. The engineer will help deliver features and improvements that realize the potential of NVIDIA hardware across workloads including deep learning, scientific computation, data science, autonomous vehicles, video games, and virtual reality. The role involves using design, coding, and creative problem-solving skills to develop the CUDA compute platform and help shape CUDA's future direction in collaboration with colleagues across NVIDIA. The engineer will develop and maintain software for multiple operating systems and contribute to CUDA APIs, programming models, and GPU scheduling capabilities.

Requirements

Bachelor's or master's degree in Computer Science, Electrical Engineering, or a related field, or equivalent experience.
Strong C and C++ programming skills.
At least 4 years of related development experience; positions are available for varying experience levels.
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, including interconnects, memory hierarchy, interrupts, and memory-mapped I/O.
Experience writing and debugging multithreaded programs.
Good written communication and presentation skills.

Nice To Haves

Prior parallel computing experience, preferably writing CUDA programs or libraries that use CUDA.
Knowledge of memory coherence and consistency models.
Background with kernel-mode development.
Experience with Linux systems software development.
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 CUDA APIs and the programming model.
Extend CUDA programming models and functionality, including CUDA Graphs.
Explore using Graphs to improve the scheduling efficiency and speed of AI/ML workloads on GPUs.
Write effective, maintainable, and well-tested code.
Develop code for multiple operating systems.