Senior System Software Engineer – Linux-Tegra Power Management and Performance Optimization

NVIDIA•Santa Clara, CA

4d•$184,000 - $287,500

About The Position

NVIDIA is seeking a Senior System Software Engineer to work with Tegra SoCs and NVIDIA embedded platforms. The role focuses on implementing power and thermal management features in the Linux kernel and user space, and optimizing system performance for edge AI and robotics. The engineer will collaborate with power architects and hardware and software engineers on platform power estimation and optimization. Work includes improving compute and memory utilization, reducing latency and power consumption, and tuning system-level performance to support reliable, scalable AI workloads in demanding real-world edge environments.

Requirements

Master’s degree in Computer Science, Computer Engineering, Electrical Engineering, Systems Engineering, or a related software or hardware engineering major, or equivalent experience.
8+ years of software development experience with a significant focus on Linux.
Excellent C programming and debugging skills within Linux kernel and user-space software.
Background working with embedded systems and ARM processors.
System-level debugging experience and problem-solving skills.
Excellent communication skills.

Nice To Haves

Understanding of Linux power and thermal management features, including scheduler, dynamic frequency scaling, runtime power management, suspend to RAM, and thermal zones, as well as factors influencing chip and platform power efficiency.
Experience working with Linux and open-source software communities.

Responsibilities

Implement power and thermal management software features in the Linux kernel and user space.
Collaborate with power architects and hardware and software engineers on platform power estimation and optimization.
Optimize the software stack for performance, efficiency, and responsiveness in edge AI and robotics use cases.
Improve compute and memory utilization, reduce latency and power consumption, and tune system-level performance for AI workloads.