Senior AI Infra Test Development Engineer, SDET
NVIDIA
China, ShanghaiPosted Discovered via the company's careers siteMatch locked
Full time5+ years exp
About the role
NVIDIA is well positioned as the 'AI Computing Company', our GPUs being the brains that power modern Deep Learning software frameworks, accelerated analytics, modern data centers, and driving autonomous vehicles. We are looking for a Senior AI Infrastructure Test Development Engineer to join in the mission of crafting a distributed technology for all NVIDIA teams that remotely manage 10s of 1000s of resources in a simple and controlled fashion, allowing engineers to focus on engineering and automation, rather than being burdened by manual operational tasks.
The AI infra test developer engineers at NVIDIA are responsible for creating test plans, execution, and reporting, as well as developing scripts for test automation, designing and developing tools for the QA team, and developing integration tests for validation. As a test developer, you must identify weak spots and constantly design better and more creative test plans to break software and identify potential issues. You will have a huge impact on the quality of NVIDIA's products.
The ideal candidate must have strong programming skills and hands-on experience using AI development tools to improve quality and productivity across the end-to-end QA workflow. This includes leveraging AI assistants for test automation, code generation, debugging, and enhancing testing efficiency. During the interview process, we will assess your ability to effectively use AI development tools and evaluate your programming capabilities to ensure you can deliver high-quality solutions.
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.
NVIDIA is at the forefront of breakthroughs in Artificial Intelligence, High-Performance Computing, and Visualization. Our teams are composed of driven, innovative professionals dedicated to pushing the boundaries of technology. We offer highly competitive salaries, an extensive benefits package, and a work environment that promotes diversity, inclusion, and flexibility. As an equal opportunity employer, we are committed to fostering a supportive and empowering workplace for all.
Not included in the source posting: about the role, qualifications, benefits.
Skills
artificial-intelligencekuberneteslinuxqadeep-learningdockerpython
Who can apply
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