Principal Engineer, CUDA UMD - GPU Kernel Scheduling at NVIDIA

Santa Clara, California, United States -

Full Time

Start Date

Immediate

Expiry Date

26 Sep, 26

Salary

431250.0

Posted On

28 Jun, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

C, C++, CUDA, Device Drivers, Multithreading, Operating Systems, GPU Kernel Scheduling, System Architecture, Linux Systems Software, Parallel Computing, Virtual Memory, Process Control

Industry

Computer Hardware Manufacturing

Description

NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. We're looking to grow our company, and form teams with the smartest people in the world. Join us at the forefront of technological advancement. Are you a motivated system software engineer with a deep understanding of device drivers who has phenomenal C/C++ skills? If so, this role might be for you. We are looking for a seasoned software professional to work on the CUDA Driver, a core component of our platform for accelerating general purpose computation on the GPU. You will be an integral part of a team that delivers features and improvements to better realize the potential of NVIDIA hardware for a growing range of computational workloads, ranging from deep learning, scientific computation, data science and self-driving cars to video games and virtual reality. What you'll be doing: As a member of our team, you will use your design abilities, coding expertise, and creativity to deliver the best compute platform in the world. You will craft elegant solutions to exciting problems and shape the future direction of CUDA as you collaborate with your peers across NVIDIA. 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 What we need to see: 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 (multiple positions for varying experience levels open) 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 Ways to stand out from the crowd: 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 Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 272,000 USD - 431,250 USD. You will also be eligible for equity and benefits. Applications for this job will be accepted at least until July 1, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law. NVIDIA pioneered accelerated computing. Today, our AI infrastructure powers global intelligence, transforming every industry. Learn more about NVIDIA.

Responsibilities

Architect and implement new features for the CUDA Driver to optimize GPU kernel scheduling for AI/ML and scientific workloads. Collaborate across teams to extend CUDA programming models and improve the efficiency of the compute platform.