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# Senior Software Development Engineer in Test - NPI Bring-up SDET

- **Company:** NVIDIA Ltd.
- **Location:** United States
- **Experience:** Expert
- **Salary:** $168,000.0 - $270,250.0
- **Contract:** Permanent contract
- **Skills:** Testing (Software), C (Programming Language), Microsoft Windows, Artificial Intelligence, Application Layers, Computing Platforms, Automation of Tests, Bash Shell, Software Bug Management, C++ (Programming Language), Configuration Management, Software Quality, Nvidia CUDA, Computer Programming, Continuous Integration, Data Centers, Software Debugging, Linux, Device Drivers, Firmware, Python (Programming Language), Log Analysis, PCI Express, Software Engineering, Virtualization Technology, Graphics Processing Unit (GPU), Computer Network Operations, Cloud Platform System, Parallel Computation, Gitlab, Bug Reporting, Information Technology, Api Management, Server Operating Systems & Platforms, SDET, Jenkins
- **Published:** October 27, 2026
- **Apply:** https://www.careerbuilder.com/job-details/senior-software-development-engineer-in-test-npi-bring-up-sdet-ca--b2683315-98a9-4d8d-84d8-ed268ada2a31

## About the Role

Software Design for Test (SDET), Product/Service Launch, Software Engineering, Software Testing, Software Engineering, Analysis Skills, Device Drivers, Silicon Bringup, Network Operations Center, CUDA (Compute Unified Device Architecture), * 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.

## 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.