You will design, develop, and execute tests across the embedded software and hardware stack, leveraging AI tools to automate end-to-end QA workflows. Strong hands-on experience with embedded platforms, automation frameworks, and coverage analysis is highly desirable. This role involves close collaboration with engineering and release teams across multiple product verticals. Join us at the forefront of the Embedded and AI revolution and help shape the future of intelligent systems. You can read more about it here: nvidia/en-us/ai/
What youll be doing:
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Using AI-powered tools to enable and implement CI/CD pipelines, optimize test execution, and drive scalable automation across workflows.
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Adding new automation to improve code coverage and overall product quality; identifying gaps and contributing to near-100% automation goals.
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Developing test strategies and implementing tests on embedded/GPU-based platforms (Jetson/embedded Linux) to ensure performance, stability, and reliability.
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Crafting detailed, well-structured test plans, test cases, and validation workflows derived from high-level customer and product requirements.
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Employing various approaches to train, re-train, and fine-tune models for accuracy, performance, and efficiency optimization in agents/workflows.
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Identifying, documenting, triaging, and managing the lifecycle of regression bugs (internal and external).
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Participating in feature requirement reviews and influencing technical build documents with data-driven QA insights.
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Coordinating with project management, hardware, and software teams to provide in-depth technical analysis of top issues; periodically publishing statistical QA health and coverage reports.
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Developing applications, test tools, utilities, and automating workflows using NVIDIA s internal test frameworks.
What we need to see:
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B. Tech. /M. Tech. in CS/CE/IT/ECE/EEE or equivalent degree/experience.
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5+ years of hands-on testing experience across the embedded software stack (bootloader, kernel, drivers, middleware, and applications).
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Strong programming skills in Python and/or C/C++; ability to write clean, logical code and scripts from scratch.
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Solid understanding of code coverage tools, test gap analysis, and quality metrics.
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Excellent knowledge of modern technologies such as Docker, AWS/cloud platforms, and front-end interfaces (HTML, REST, web automation).
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Strong exposure to Linux, shell scripting, and debugging complex system behaviors.
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Proven ability to collaborate across cross-functional teams and passion for new technologies, frameworks, and trends.
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Hands-on experience testing on embedded hardware platforms (preferably ARM/Linux or GPU-accelerated systems).
Ways to stand out from the crowd:
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A degree or certification in Machine Learning / Artificial Intelligence.
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Experience with deployment technologies like Kubernetes or container orchestration.
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Understanding of workflow/agent-based test automation, model validation, and quality assurance systems powered by machine learning.
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Experience improving test execution efficiency (e. g. , stability tuning, pipeline optimization, automation run-rate growth).
