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

- **Company:** NVIDIA Corporation
- **Location:** Santa Clara, CA, United States
- **Experience:** Expert
- **Salary:** $184,000.0 - $287,500.0
- **Contract:** Permanent contract
- **Skills:** C (Programming Language), Systems Engineering, ARM Architecture, Computer Engineering, Software Debugging, Linux, Linux Kernel, Performance Tuning, Software Engineering, System Software, Free and Open-Source Software
- **Published:** September 10, 2026
- **Apply:** https://nvidia.wd5.myworkdayjobs.com/NVIDIAExternalCareerSite/job/US-CA-Santa-Clara/Senior-System-Software-Engineer---Linux-Tegra-Power-Management-and-Performance-Optimization_JR2025344

## About the Role

* MS in CS, CE, EE, Systems Engineering or related software/hardware engineering major, or equivalent experience
 * 8+ years of software development experience with a significant focus on Linux
 * Excellent C programming/debugging skills within Linux kernel and user space software
 * Background with working on embedded systems and ARM processor specific
 * System-level debugging experience and problem-solving skills
 * Excellent communication skills

Ways to stand out from the crowd:

 * Understanding of the Linux power and thermal management features (scheduler, dynamic frequency scaling, runtime power management, suspend to RAM, thermal-zones ..etc), factors influencing power efficiency at chip and platform.
 * Experience in working with the Linux and open-source software communities

## Description

* You will work with ground breaking technologies for the Tegra SoC and various NVIDIA embedded platforms
 * Implement power and thermal management software features in Linux Kernel and user space
 * Collaborate with power architects, hardware and software engineers on platform power estimation and optimization
 * Optimize the software stack to improve performance, efficiency, and responsiveness for edge AI and robotics use cases. Focus on improving compute and memory utilization, reducing latency and power consumption, and tuning system-level performance to deliver reliable and scalable AI workloads across demanding real-world edge environments.


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