Nvidia Senior Software QA Test Development Engineer 

United States, California 

217770998

What you’ll be doing:

• 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.

What we need to see:

• 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 inGitHub/Gitlab/Gerrit,PXE, SLURM,Stack/Kubernetes/Docker)– huge plus

Ways to stand out from the crowd:

• 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