Are you ready to do your life’s work at the heart of the autonomous revolution? NVIDIA’s SWQA organization is seeking a world-class Software QA Test and Tool Developer to join our Automotive Platform team, where the code you validate ensures the safety of millions on the road. In this role, you won't just be testing software; you will be architecting the security and reliability of the next generation of intelligent vehicles. We are looking for engineers who are as comfortable navigating low-level product architecture as they are deep diving into complex product use cases with passion for quality. This is a high-impact, hands-on position focused on our industry-leading automotive products, offering a rare opportunity to influence the core of our tech stack. You will also build the tools and frameworks that define performance standards for systems running on Linux and QNX.
What you’ll be doing:
Design, execute, and automate comprehensive test cases and test scenarios to validate our automotive platforms using various test methodologies to identify and track actionable defects and track them to closure.
Participate in deep-dive reviews of product requirements and technical designs, providing critical feedback to ensure features are built for testability and security from day one.
Partner closely with project management, hardware teams, and software developers to provide rigorous technical analysis of bugs and publish data-driven statistical reports for global team members.
Architect and maintain a distributed test automation framework capable of managing high concurrency workloads across an extensive automation farm of hundreds of concurrent systems.
Develop sophisticated test libraries and automation solutions to accelerate development cycles and expand automated test coverage for reliable and 100%
Drive the full automation lifecycle, from analysing log failures and logging defects to leading bug-scrub cycles that ensure high-quality product releases.
What we need to see:
5+ years of proven experience in SWQA Test development & Automation engineering.
Bachelor's degree in computer science, Electronics & Electrical Engineering. Solid foundation in QNX or Linux-based operating systems, including a detailed understanding of system concepts and boot sequences.
Professional experience in System SW validation, Including CPU, Memory, GPU, Ethernet, CAN etc. Knowledge about Ethernet standards, TCP, UDP, ICMP, Switches, socket programming.
Strong Python or C++ skills with a focus on writing clean, maintainable, and testable code from a systems-level perspective.
Deep familiarity with AI-native development tools such as Claude Code, Cursor, or LLM APIs to optimize engineering velocity.
A clear understanding of LLM failure modes—including hallucination and context degradation—and experience building evaluation frameworks for AI-generated outputs.
Ways to stand out from the crowd:
Prior QNX, Linux testing experience especially in 1G & 10G Ethernet Testing & Socket programming.
C / C++ coding experience. Understand the large C / C++ project code and derive functional/unit tests.
Practical history of deploying prompt engineering or LLM-based agents within a production grade CI/CD pipeline.
With competitive salaries and a generous benefits package, we are widely considered to be one of the technology world’s most desirable employers. If you're a creative and autonomous engineer with a real passion for technology, we want to hear from you. Come build the future with us! We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, colour, national origin, gender, sexual orientation, age, marital status.
Skills Required
- 5+ years of proven experience in SWQA test development and automation engineering
- Bachelor's degree in Computer Science, Electronics or Electrical Engineering
- Solid foundation in QNX or Linux-based operating systems, including system concepts and boot sequences
- Professional experience in system software validation including CPU, Memory, GPU, Ethernet, CAN
- Knowledge of Ethernet standards, TCP, UDP, ICMP, network switches, and socket programming
- Strong Python or C++ skills focused on writing clean, maintainable, testable systems-level code
- Deep familiarity with AI-native development tools such as Claude Code, Cursor, or LLM APIs
- Understanding of LLM failure modes (hallucination, context degradation) and experience building evaluation frameworks for AI outputs
- Experience architecting and maintaining distributed test automation frameworks managing high-concurrency automation farms
- Prior QNX/Linux testing experience, especially 1G & 10G Ethernet testing and socket programming
- C / C++ coding experience understanding large projects and deriving functional/unit tests
- Practical history deploying prompt engineering or LLM-based agents within production-grade CI/CD pipelines
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
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