Principal Engineer, CUDA UMD - GPU Kernel Scheduling

Nvidia

Confirmed live today Trusted

Closes in 5 days

Quick summary

Work type: On-site
Location: Santa Clara, CA
Salary: $272,000–$431,250 / yr
Employment: Full-time
Posted: 43 days ago
Freshness: Confirmed live today
Closes: Nov 1, 2026 (soon)
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $222k

This role $352k

$134k $463k

below market most similar roles pay here above market

This role pays more than 99% of similar roles. Most pay $196,750–$247,150 — the blue band above. At the midpoint, this role pays about $352k versus about $222k for comparable roles.

Based on 240 similar postings.

Employer

About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 1623 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 1110 roles with salary data.

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At a glance

TL;DR · Principal Engineer, CUDA UMD - GPU Kernel Scheduling

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The Principal Engineer, CUDA UMD - GPU Kernel Scheduling joins a team focused on the CUDA Driver, a core platform component for accelerating general-purpose computation on the GPU. This role involves architecting, evangelizing, and implementing new features to improve hardware potential for deep learning, scientific computation, and virtual reality. Key responsibilities include defining forward-looking improvements to CUDA APIs, extending programming models like CUDA Graphs, and exploring efficient scheduling for AI/ML workloads. The successful candidate will write maintainable code for multiple operating systems while managing projects across various teams. Required skills include expert C and C++ programming, experience with operating system interfaces for threads and virtual memory, and multithreaded debugging. Preferred expertise includes parallel computing, kernel mode development, and system-level architecture knowledge like memory coherence and interconnects.

What does a Engineer earn in California?

Median $199500 from 103 postings across 31 companies.

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Skills

C C++ CUDA Device Drivers Parallel Computing Linux Kernel Mode Development Virtual Memory System Software Operating System Interfaces Memory Hierarchy Memory-Mapped IO CUDA Graphs AI/ML Workloads

What you'll do

Architect, evangelize, and implement new features for the CUDA Driver.
Coordinate and drive development efforts across multiple engineering teams.
Define forward-looking improvements to CUDA APIs and the programming model.
Extend CUDA programming models and functionality, specifically CUDA Graphs.
Research ways to use Graphs to improve AI/ML workload scheduling on GPUs.
Write effective, maintainable, and well-tested code for multiple operating systems.
Develop and debug multithreaded programs using C and C++.

What we're looking for

BS or MS degree in Computer Science, Electrical Engineering, or a 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 operating system interfaces for threads, process control, and virtual memory.
Experience writing and debugging multithreaded programs.
Good written communication and presentation skills.
Prior experience with parallel computing, such as writing CUDA programs or libraries (preferred).

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