Networking QA and Automation Engineer, Network Systems

NVIDIA is looking for a Software QA Engineer with a strong background in Networking and Automation to join our InfiniBand (IB) and NVLINK (NVL) Switch QA team. Our team is responsible for qualifying software stack for NVIDIA’s IB Switch, Router, Gateway and NVLINK systems, delivering world-class networking solutions.

You will work at the heart of cutting-edge technology, validating software management features, designing topologies, developing automated test suites, and collaborating with engineering and product teams to ensure delivery of robust and scalable systems.

What you’ll be doing:

Design, develop, and execute manual and automated tests as part of software stack releases.

Define, build, and manage testbed topologies for functional, regression, and performance validation.

Analyze architectural designs and feature requirements for new networking capabilities.

Debug failures, identify root causes, and verify fixes delivered by development teams.

Schedule test runs, track testing progress, and generate test status reports with detailed defect documentation.

Write and maintain automation tests across multiple frameworks (Python, Perl), enhancing test efficiency and scalability.

Collaborate with cross-functional global teams including R&D, product marketing, and system verification.

What we need to see:

B.Sc. in Computer Science, Information Systems, Electrical Engineering, or related technical field.

1-3 years of hands-on experience in the field of QA testing

Strong understanding of software testing methodologies, test planning, and bug lifecycle.

Hands-on experience in automation scripting (Python, Perl, or Shell) on Unix/Linux platforms.

Familiarity with networking concepts, protocols, and devices (e.g., switches, NICs).

Strong analytical and debugging skills with an eye for detail.

Clear verbal, proficient written and spoken English

Ways to stand out from the crowd:

Experience in Python automation and working with source control tools (Git, Gerrit), Solid knowledge of Linux and kernel internals.

Hands-on experience with virtualized and mixed computing environments (KVM, VMware, Linux/Windows).

Experience using Generative AI platforms / LLMs such as Gemini, Claude, Copilot, etc. to develop tools powered by artificial intelligence.

In-depth understanding of TCP/IP, routing protocols, LAN switching, and data center topologies.

Exposure to QA methodologies, release management, and end-to-end test lifecycle. Familiarity with NVIDIA technologies such as Infiniband, NVLINK, GPUs is a strong advantage.