GPU Software Engineer Intern

NVIDIA is searching for a highly motivated, creative engineer with experience in system software to join the GPU Software team.

In this role, you will contribute to key aspects of our production GPU kernel drivers, SoC platform software, and embedded system software.

Responsibilities

• Define, design, develop, and verify features for next-generation GPU chips and SoC platforms, collaborating with hardware architects, system engineers, and software engineers across teams.
• Contribute to early modeling, simulation, bring-up, and validation required to deliver world-class GPU and SoC products.
• Develop and enhance low-level system software, including kernel drivers, firmware interfaces, platform software, performance features, and power management.
• Support project work across core GPU kernel software, SoC platform software, or related system software areas based on team needs and internship scope.
• Contribute to adjacent technical areas such as platform security, confidential computing, AI-related workflows, debugging, automation, telemetry analysis, or system software robustness when relevant to the assigned project.
• Collaborate and communicate effectively with teams across multiple geographic regions throughout the product development lifecycle.

Requirements

• Currently pursuing a BS, MS, or PhD in Computer Engineering, Computer Science, Electrical Engineering, or a related field.
• Strong programming skills in C and C++, with experience in low-level driver development and system software on x86, ARM, or SoC platforms.
• Familiarity with computer architecture, operating system fundamentals, microprocessors, and microcontrollers.
• Experience with Linux, Android, ChromeOS, or Windows kernel and system software environments.
• Knowledge of system-level debugging, root-cause analysis, and performance or stability troubleshooting in complex hardware/software systems.

Nice to Have

• Exposure to or interest in software security, confidential computing, secure software design, or platform security concepts.
• Exposure to or interest in AI/ML workflows, AI infrastructure, data processing, or AI-assisted software development and debugging tools.
• Strong background in GPU, SoC, system architecture, or platform software.
• Deep understanding of device drivers, memory management, firmware, power management, performance tuning, and system reliability.
• Coursework, project, or internship experience in security-related areas such as secure boot, confidential computing, firmware security, trusted execution, or OS hardening.
• Familiarity with AI/ML frameworks, AI computing workloads, profiling, telemetry analytics, or using AI-based tools to improve engineering productivity.
• Experience building or maintaining complex system software, platform software, open-source software, or large-scale build and test flows and experience delivering embedded, platform, or system software in RTOS, Linux, Android, Windows, x86, ARM, or SoC environments.