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Senior Software QA Test Development Engineer
| Company | NVIDIA |
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| Location | Santa Clara, CA, USA |
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| Salary | $136000 – $264500 |
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| Type | Full-Time |
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| Degrees | Bachelor’s, Master’s |
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| Experience Level | Senior |
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Requirements
- Bachelor’s Degree (or equivalent experience) in a STEM (Science, Technology, Engineering, Math or Physics) field
- 5+ years proven experience; or master’s degree
- Proven years of OS and server level automation, CI/CD process and DevOps experience using Python, SHELL, Ansible, Jenkins, C/C++, Java, JavaScript
- Strong server and Linux(Ubuntu, RedHat, CentOS, SuSE, Fedora and etc…) troubleshooting and debugging experience in a bare-metal and KVM/VMWare/Hyper-V environment
- Good knowledge and hands-on experience in model testing, AI tools/frameworks (TensorFlow, Pytorch, Cursor and etc…), NLP and LLM benchmarking
- Experience in using AI development tools for test plans creation, test cases development and test cases automation
- Strong experience in FW, BMC/OpenBMC, Network protocol, internal/external enterprise storage devices, PCIe buses and devices, IO sub-devices, CPU and memory, ACPI, UEFI spec, Redfish – huge plus
- Proven years of experience in GitHub/Gitlab/Gerrit, PXE, SLURM, Stack/Kubernetes/Docker) – huge plus
Responsibilities
- Responsible for the development and execution of NVIDIA HGX/DGX/MGX platform test plan on servers, OS, FW and CUDA SW stack from design doc
- Installing and testing various systems OS, server firmware and SW stack
- Drive support for root cause analysis on reliability and validation test failures to identify root cause(s) and achieve mitigation
- Build, develop/debug server and OS level automation front-end and back-end framework and tests
- Review partner and supplier test results and prescribe additional reliability testing on components, servers, and packaging as needed
- Work in an agile software development team with very high production quality standards
- Manage bug lifecycle and collaborate with inter-groups to drive for solutions
Preferred Qualifications
- AI related tools, LLM and NLP
- Experience working with NVIDIA GPU hardware is a strong plus
- Good to have solid understanding of virtualization in Linux (KVM, Docker orchestrated with Kubernetes)
- Background in parallel programming ideally CUDA/OpenCL is a plus
