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.What you’ll be doing:Utilizing AI-powered tools to enhance QA efficiency, including automating test case generation, defect detection, and regression testing.Implementing AI-driven solutions to optimize test coverage and identify high-risk areas in software systems.Collaborating with cross-functional teams to adopt AI tools that improve workflow automation and reduce manual effort.Review product requirements and collaborate with cross-functional teams to define test requirements/strategiesBuild test plan, design test case, execute and report test progress, bugs and results to management.Perform Function, Performance, Fault Injection and reliability testingAutomate test cases and assist in the architecture, crafting and implementing of test frameworks.Manage bug lifecycle and co-work with inter-groups to drive for solutions.In-house repro and verify customer issues/fixes.Leveraging AI-powered tools to automate repetitive testing tasks, optimize test coverage, and detect flaky testsWhat we need to see:BS or higher degree or equivalent experience in CS/EE/CE with 5+ years QA experience.Experience using AI tools for QA tasksFamiliarity with AI-powered testing frameworks and platforms that improve process efficiency Strong understanding of QA methodologies and the ability to integrate AI tools into existing workflowsProficient in Unix/Linux and shell/python programming skills with a strong understanding of Kubernetes architecture and its componentsExperience with containerization technologiesExperience with CI/CD pipelines and toolsHands-on experience in testing observability solutions (Prometheus, Grafana, ELK Stack, Datadog, etc.) and validating metrics, logs, traces, and alerting workflowsProven experience in test cases development, tests automation and failure analysis, preferably with Kubernetes or cloud-based services, simulating large clusters and testing various failures (tools like KWOK and chaos monkey, etc.)Good QA sense, knowledge, and experience in software testing who is an excellent communicator, fluent written and verbal English.Ways to stand out from the crowd:Proven success in leveraging AI tools to significantly reduce testing time or improve defect detection rates.Experience in implementing innovative AI-driven solutions that streamline QA workflows or enhance process automation.Background in SaaS and PaaS test is a strong plusExperience working with NVIDIA GPU hardware is a strong plus