US, California, Benefits
- Equity
Full job description
We are seeking a Senior Software Engineer in Test to join the Compute CUDA Quality Assurance team and drive validation readiness for PCIe Data Center GPU programs. This role combines new silicon bring up, CUDA and NVIDIA Driver quality, automation development, test infrastructure, and AI assisted quality analysis. Join the team and help advance the next generation of NVIDIA Data Center GPUs!
We lead test planning, execution readiness, defect triage, and automation from early platform bring up through key release stages. We collaborate across CUDA, NVIDIA Driver, GPU architecture, firmware, system platform, Quality Assurance, and program teams to translate feature requirements into measurable coverage, identify release risks, and drive issues to resolution.
What you'll be doing:
Develop CUDA Compute test plans and coordinate validation across PCIe high-performance computing GPUs, NVIDIA systems, OEM servers, GPU configurations, operating systems, driver modes, CUDA versions, and release stages.
Build Python, C, C++, CUDA, and Bash test applications, automation frameworks, release configurations, and GitLab or Jenkins pipelines. Convert NPI failures, customer defects, root cause findings, and suitable manual tests into reliable automated regression coverage.
Separate product regressions from automation, infrastructure, configuration, and intermittent issues. Drive defects with clear reproduction steps, affected configurations, intended and actual results, supporting evidence, release impact, fix verification, and adjacent regression coverage.
Apply AI assisted tools to analyze test runs and validate conclusions against source logs and engineering decisions.
What we need to see:
A BS or MS in Engineering, Computer Science, or a related field, or equivalent experience, with 8+ years experience in software quality, test automation, or software development and validation.
Experience solving Linux and Windows system issues; development using scripting and programming languages such as Python, C, C++, CUDA, or Bash; and debugging across hardware, firmware, operating system, driver, and application layers.
Knowledge of Quality Assurance methodology, risk based test planning, functional validation, regression strategy, release readiness, CI/CD, API integrations, log analysis, root cause isolation, and defect triage.
Strong communication and teamwork skills are essential for explaining release risk and clarifying ownership, dependencies, and blockers.
Ways to stand out from the crowd:
Experience with NVIDIA GPU hardware, CUDA, PCIe-based GPUs built for data centers, server platforms, CUDA C or C++ parallel programming, multiple GPU validation, P2P, reliability, or stress testing.
Experience with developing scalable validation infrastructure for labs, cloud environments, containers, virtualization, or configuration management.
Practical use of AI assisted analytics for test planning, automation, failure analysis, or defect detection is also valued.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until November 3, 2026.This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Opens NVIDIA’s own application page in a new tab.
