What if your Python automation caught a critical ASIC bug before the physical silicon was even manufactured? At NVIDIA, our simulation platforms make that happen every release cycle! We are the System Ethernet QA Team at NVIDIA's R&D center, responsible for the testing and qualification of virtual networking silicon (SimX) and software—from L2 forwarding and L3 routing to overlays, QoS, and security. With SimX, developers and QA engineers get hardware-free, early access to the ASIC for better debugging.
We are looking for a Senior QA Software Engineer who combines network protocol expertise with strong Python automation skills. Does debugging a BGP EVPN route leak excite you as much as refactoring a test framework? Then we should talk!
What you'll be doing:
We don't just complete checklists; we collaborate closely with R&D to lead the quality of new features from design to deployment. In this role, we:
Review architectural designs and requirements for new features introduced to the product
Drive the design, development, and execution of tests across functional, performance, and security scopes as part of SW GA and update releases
Develop Python automation suites built on clean OOP principles
Chase down defects and validate fixes until we fully resolve the root cause
Leverage the SimX platform to validate code and debug the ASIC long before hardware arrives
What We Need to See:
Combined network engineering depth with thorough software habits
5+ years of hands-on QA or networking experience, backed by CCNP-equivalent protocol knowledge: L2 (VLAN, STP), L3 (BGP, OSPF), EVPN, VXLAN, and advanced features like PFC, ECN, RoCE, and QoS
Production-quality Python engineering—specifically, strong OOP design and hands-on experience with Pytest
Deep Linux proficiency (at an LPIC-1 or LPIC-2 level) to navigate system internals, analyze logs, and debug effectively
Clear verbal and written communication, with professional proficiency in written and spoken English
B.Sc. or or B.A. or equivalent experience in Computer Science, Electrical Engineering, or a similar background
Ways to stand out from the crowd:
Experience with Docker and traffic generation tools (Ixia, TRex, Scapy)
Hands-on experience integrating tests into CI/CD pipelines (Jenkins, GitLab CI)
Working knowledge of virtualization platforms (KVM, VMware)
157,500 PLN – 273,000 PLN for Level 3; and 206,250 PLN – 357,500 PLN for Level 4.
Skills Required
- 5+ years of hands-on QA or networking experience
- CCNP-equivalent networking protocol knowledge, including L2, L3, EVPN, VXLAN, PFC, ECN, RoCE, and QoS
- Production-quality Python engineering experience with strong object-oriented design
- Hands-on experience with Pytest
- Deep Linux proficiency at an LPIC-1 or LPIC-2 level
- Professional proficiency in written and spoken English
- Bachelor's degree in Computer Science, Electrical Engineering, or a similar field, or equivalent experience
- Experience with Docker and traffic generation tools such as Ixia, TRex, or Scapy
- Experience integrating tests into CI/CD pipelines using Jenkins or GitLab CI
- Working knowledge of KVM or VMware virtualization platforms
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Feedback suggests equity grants, stock options/RSUs, and a discounted ESPP materially boost total compensation and can deliver significant wealth-building when the stock performs well.
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Healthcare Strength — Feedback suggests medical, dental, and vision plans with in-network preventive care at 100%, telehealth, and concierge-style support are comprehensive and valued.
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Parental & Family Support — Feedback suggests up to 22 weeks fully paid parental leave, fertility coverage, and adoption/surrogacy reimbursement provide strong family-focused benefits.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
