Network Solution Verification Engineer

Posted 7 Days Ago
Be an Early Applicant
2 Locations
In-Office
Mid level
Artificial Intelligence • Computer Vision • Hardware • Robotics • Metaverse
The Role
Design and implement large-scale end-to-end network validation frameworks and automated regression pipelines. Build simulation agents and traffic generators, apply agentic LLM frameworks for autonomous test generation and analysis, integrate validation into CI/CD, triage simulation results, and collaborate with design and architecture teams to close coverage gaps and prevent customer regressions.
Summary Generated by Built In

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation fueled by great technology—and amazing people. We are seeking a Network Solution Verification Engineer to join our R&D organization, focused on validating NVIDIA's customer-facing networking solutions at scale using a state-of-the-art End-to-End (E2E) simulation cluster environment. 

In this role, you will be a hands-on technical contributor at the intersection of networking, automation, and AI-driven validation — designing and implementing the simulation frameworks, agentic workflows, and regression pipelines that ensure NVIDIA's networking solutions meet the highest standards of quality and real-world applicability before reaching customers. 


What you'll be doing: 

  • Designing and implementing end-to-end validation frameworks for NVIDIA's customer-facing networking solutions at scale, leveraging a dedicated E2E simulation cluster environment. 
  • Writing, maintaining, and extending automated test suites and regression pipelines for networking protocols and large-scale simulation runs, ensuring repeatable, high-confidence validation outcomes. 
  • Performing deep regression analysis on simulation results — identifying failure trends, isolating root causes, and delivering clear, actionable findings to architecture and design teams. 
  • Developing agentic AI flows that autonomously perform regression analysis, detect coverage gaps, generate new test cases, and implement validation code — continuously learning from simulation results and product changes to accelerate coverage without manual intervention. 
  • Integrating validation pipelines into CI/CD workflows to enable continuous, automated regression at scale, working closely with DevOps and platform teams. 
  • Collaborating closely with Design, Architecture, and NCS teams to understand solution requirements, translate them into simulation scenarios, and provide early-cycle quality feedback that influences product direction. 
  • Analyzing customer-reported networking issues, mapping them to simulation coverage gaps, and building targeted test cases that prevent regression. 
  • Continuously exploring new simulation technologies, agentic frameworks, and networking standards to evolve and improve the team's validation methodology. 

What we need to see: 

  • B.Sc. degree or equivalent experience in Computer Science, Electrical Engineering, Computer Engineering, or a related field. 
  • 3+ years of hands-on experience as a software developer. 
  • Strong proficiency in Python for test automation, tooling development, and pipeline implementation. 
  • Hands-on experience with network simulation or emulation tools (e.g., Containerlab, GNS3, SONiC testbeds, or equivalent platforms). 
  • Proven experience designing and building simulation agents or traffic generators that mimic real-world networking behavior at scale. 
  • Solid experience with agentic AI frameworks and LLM-based automation (e.g., LangChain, LangGraph, AutoGen, or similar) and practical ability to apply them to validation and test generation workflows. 
  • Strong command of regression analysis methodologies — able to triage, classify, and extract actionable conclusions from large-scale test result datasets. 
  • Comfortable operating in a fast-paced, cross-functional, multi-timezone engineering environment with strong verbal and written communication skills. 

Ways to stand out from the crowd: 

  • Hands-on experience validating data center networking solutions or hyperscale network environments (spine-leaf, fat-tree, or Clos topologies). 
  • Familiarity with NVIDIA networking products — BlueField DPUs, ConnectX NICs, Spectrum switches, or the DOCA software stack. 
  • Deep understanding of networking protocols and architectures (e.g., BGP, EVPN, VXLAN, RDMA/RoCE, Ethernet, IP routing, L2/L3 switching). 
  • Hands-on experience building agentic pipelines for automated test generation, result triage, or validation code synthesis — including prompt engineering and tool-use patterns for LLM agents. 
  • Prior exposure to customer-facing solution validation or translating customer deployment scenarios into structured simulation test cases. 

With competitive salaries and a generous benefits package, NVIDIA is widely recognized as one of the technology world's most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us, and our engineering teams continue to grow rapidly to meet extraordinary demand. If you are a technically driven engineer passionate about networking, simulation, and pushing the boundaries of AI-assisted validation, we want to hear from you. 

NVIDIA is committed to fostering a diverse work environment and is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, disability status, or any other characteristic protected by law.

Skills Required

  • B.Sc. degree or equivalent experience in Computer Science, Electrical Engineering, Computer Engineering, or related field
  • 3+ years of hands-on experience as a software developer
  • Strong proficiency in Python for test automation, tooling development, and pipeline implementation
  • Hands-on experience with network simulation or emulation tools (e.g., Containerlab, GNS3, SONiC testbeds, or equivalent)
  • Proven experience designing and building simulation agents or traffic generators that mimic real-world networking behavior at scale
  • Practical experience with agentic AI frameworks and LLM-based automation (e.g., LangChain, LangGraph, AutoGen, or similar)
  • Strong command of regression analysis methodologies and ability to triage and extract actionable conclusions from large-scale test results
  • Strong verbal and written communication; comfortable in fast-paced, cross-functional, multi-timezone engineering environments
  • Hands-on experience validating data center networking solutions or hyperscale environments (spine-leaf, fat-tree, Clos topologies)
  • Familiarity with NVIDIA networking products (BlueField DPUs, ConnectX NICs, Spectrum switches, DOCA software stack)
  • Deep understanding of networking protocols and architectures (BGP, EVPN, VXLAN, RDMA/RoCE, Ethernet, IP routing, L2/L3 switching)
  • Experience building agentic pipelines for automated test generation, result triage, or validation code synthesis, including prompt engineering
  • Prior exposure to customer-facing solution validation and translating customer deployment scenarios into simulation test cases

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.

  • Equity Value & Accessibility Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
  • Healthcare Strength Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
  • Retirement Support Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.

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The Company
HQ: Santa Clara, CA
21,960 Employees
Year Founded: 1993

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.”

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