Senior AI Infrastructure Test Development Engineer

NVIDIA is seeking a Senior AI Infrastructure Test Development Engineer to craft a distributed technology for remote management of resources. The ideal candidate will have strong programming skills and hands-on experience using AI development tools.

What you'll be doing:

• Architect, implement, and evolve scalable agentic end-to-end SWQA workflow, automated test frameworks, infrastructure, and tooling for complex software products.
• Define test strategy and quality gates across functional, integration, regression, reliability, and release-validation workflows.
• Build and maintain high-value automated coverage for Linux-based, containerized, and Kubernetes environments.
• Investigate difficult failures across software stacks; isolate root causes and work directly with development teams through resolution with proposed bug fix PRs.
• Establish meaningful quality metrics, improve signal-to-noise in test results, and use data to prioritize engineering investment.
• Drive automation efficiency through well-engineered tooling, reusable frameworks, and AI-assisted workflows where they demonstrably improve quality or developer productivity.
• Raise the technical capability of the wider team through code reviews, design reviews, documentation, and mentorship.
• Take ownership of ambiguous, cross-functional quality problems and deliver durable solutions,not just test execution.

What we need to see:

• 5+ years of experience in infrastructure software development or test development. An MS or PhD in computer science or a related field is preferred.
• Strong Python programming skills, with the ability to write clean, maintainable, well-tested code.
• Hands-on experience designing end-to-end SWQA workflows and automation frameworks, including practical use of AI tools to improve test development or debugging.
• Strong Linux expertise and experience testing software deployed with Docker and Kubernetes.
• A solid understanding of risk-based testing, testability, observability, regression strategy, and release criteria.
• Familiarity with bare-metal infrastructure and some provisioning or networking technologies, such as BMC/Redfish, PXE, DPUs, or TCP/IP.
• The ability to debug multi-layer failures spanning services, infrastructure, networking, and deployment configuration.
• Demonstrated technical ownership and the communication skills to align stakeholders, deliver solutions, and measure outcomes.

Ways to stand out from the crowd:

• Created or significantly improved a test platform used by multiple teams or products.
• Reduced release risk, escaped defects, test runtime, or manual validation effort through measurable engineering improvements.
• Built and owned an AI-assisted or agentic SWQA workflow that improved coverage or efficiency.
• Validated distributed or large-scale infrastructure under realistic failure conditions.
• Hands-on experience with BlueField DPUs, Redfish, firmware management, or bare-metal provisioning.