Senior Software Engineer, CUDA UMD - Graphs and GPU Sharing

NVIDIA•Santa Clara, CA

5d•$224,000 - $356,500

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, self-driving cars, video games, and virtual reality. As part of the team, the engineer will use design, coding, and problem-solving skills to develop the CUDA compute platform and help shape CUDA's future direction while collaborating with colleagues across NVIDIA. The role includes extending CUDA programming models and functionality, including CUDA Graphs and Multi-Process Service (MPS), and developing code for multiple operating systems.

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.
A minimum of 8–10 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 and 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 I/O.
Knowledge of memory coherence and consistency models.
Background with kernel-mode development.
Experience with Linux systems software development.

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 and Multi-Process Service (MPS).
Write effective, maintainable, and well-tested code.
Develop code for multiple operating systems.