NVIDIA
From NVIDIA's careers page
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An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world.
NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.
Use and develop AI-powered tools to make software testing smarter, faster, and more effective!
Improve test case generation, defect detection, flaky test analysis, regression testing, and test coverage optimization.
Work with product, engineering, and cross-functional teams to review requirements and define test strategies.
Build test plans, design and execute test cases, and report quality status, risks, bugs, and results.
Perform functional, performance, fault-injection, reliability, and regression testing for cloud-native systems.
Automate test cases and contribute to scalable test frameworks.
Manage the bug lifecycle, reproduce customer issues, and verify fixes.
MS or PhD in Computer Science, Engineering, or a related field.
5+ years of QA, test automation, or software testing experience.
Hands-on experience using AI tools to improve QA workflows.
Strong QA fundamentals, test strategy, test planning, and failure analysis skills.
Proficiency with Unix/Linux and shell or Python programming.
Experience with Kubernetes, containers, CI/CD pipelines, and cloud-native systems.
Experience testing observability tools such as Prometheus, Grafana, ELK Stack, Datadog, or similar platforms.
Experience with large-scale failure testing, using tools such as KWOK, Chaos Monkey, or similar frameworks.
Strong communication, problem-solving skills, and curiosity for complex technology
Used AI to reduce testing time, improve defect detection, or increase test coverage.
Built AI-driven solutions that streamlined QA workflows or reduced manual effort.
Experience with NVIDIA GPU hardware or GPU-accelerated software.
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