Senior System Software Engineer - Halos

NVIDIA is hiring a Senior System Software Engineer - Halos in Santa Clara, United States. It pays $184k-$288k a year and Level rates it ; you can apply on NVIDIA.

Level's read on this job

Little AI ●○○○ is how Level rates this software engineer role: aI is not part of the work.
Pay on this posting starts 10% higher than the typical software engineer posting on Level.
Level classes it as a senior role.
NVIDIA has 2,700+ other open roles on Level.

AI in this role

We are looking for a talented Senior System Software Engineer to help build NVIDIA Halos, our full-stack safety platform for physical AI: autonomous vehicles, humanoids, industrial robots, and intelligent machines that sense, decide, and act in the real world. This role sits at the intersection of embedded systems, rich operating systems, safety, security, virtualization, and accelerated computing. If you have a good understanding of System Software development on Real Time OS (RTOS), ARM architecture, Virtualization, strong C and/or Ada/SPARK programming skills, Automotive Networking Solutions, Time Synchronization, and experience with formal methods, we want to hear from you! We are a leading artificial intelligence computing company and are paving the way with innovations in gaming, visualization, supercomputing, and autonomous vehicle and robotics safety platforms.

NVIDIA gives automakers, tier-1 suppliers, automotive research institutions, and startups the power and flexibility to develop and deploy breakthrough artificial intelligence systems for autonomous vehicles. Our unified computing architecture makes it possible to train deep neural networks in the data center on the NVIDIA DGX™, and then seamlessly run them on NVIDIA's Halos platform inside the vehicle. Leading vehicle manufacturers, tier 1 suppliers, mapping and simulation companies, software and sensor providers, and startups around the world are developing on the NVIDIA Halos® platform to deliver the best solutions for the new world of mobility. As a System Software Engineer, you will get an opportunity to work on high-performance NVIDIA edge AI platforms running complex mixed-criticality software stacks across QNX, Linux, hypervisors, safety partitions, accelerators, and real-time AI workloads, and work alongside industry experts in diverse teams and projects.

What you’ll be doing:

Architect, design, develop, and debug system software for NVIDIA Halos OS across QNX, Linux, and hypervisor-based environments.
Design and develop BSP, boot, driver, device-management, networking, diagnostics and platform software for NVIDIA SoCs and edge AI platforms.
Develop safe and secure software in accordance with ISO 26262 and ISO 21434 standards.
Bring up and optimize next-generation NVIDIA platforms, working closely with ASIC, firmware, OS, safety, security, platform teams and external vendors.
Design & develop I/O Virtualization solutions for mixed-criticality systems
Use Formal Methods to architect and develop high integrity software
Develop and use GenAI and AI-assisted engineering tools to accelerate debugging, test development, code review, documentation, requirements analysis, and workflow automation.
Resolve complex kernel-level issues and lead integration of external Ethernet switching technology for next-generation vehicle platforms.

What we need to see:

Bachelors/master’s or equivalent experience in Computer Science
8+ years of experience in System Software architecture and development for real time embedded systems
Strong understanding of computer architecture, operating system fundamentals, and Networking.
Proficient programming and debugging abilities in Python/C and/or Ada/SPARK
Practical experience using AI-assisted development tools for coding, debugging, testing, documentation, or workflow automation.
Hands-on experience with debugging tools, performance analysis, system and Network analyzer tools and Platform bring up
Deep expertise in automotive networking across Linux and QNX, including network stack optimization, multi-OS integration, IEEE 1588 PTP, TSN, hardware time-stamping, Ethernet switches, PHYs, and cross-layer debug.
A dedicated, team and results oriented, self-motivated contributor with excellent analytical and interpersonal skills

Ways to stand out from the crowd:

In-depth knowledge of embedded systems and real time operating systems, like QNX and RT Linux skills
Good understanding of ARM architecture and low-level software development for ARM v8 based SOCs
Prior hands-on experience in Ada/SPARK programming (including specification and formal verification) and TLA+ formal verification modeling
Experience in software development adhering to ISO 26262 and ISO 21434 standards

Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are:
184,000 USD – 287,500 USD for Level 4; and 224,000 USD – 356,500 USD for Level 5.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until November 3, 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.

How we rate this

Senior System Software Engineer - Halos at NVIDIA rates 7 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.

Classification

Little AI. AI is not part of the work.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.