Network Solution Verification Manager

NVIDIA NVIDIA · Semiconductors · Tel Aviv, Israel +1

NVIDIA is seeking a Network Solution Verification Manager to lead a team in validating customer-facing networking solutions using simulation. The role involves pioneering the use of agentic AI flows for autonomous regression analysis, test case generation, and validation code implementation, aiming to accelerate test coverage and improve quality signals. The manager will define validation strategy, oversee automated regression suites, and collaborate with engineering teams.

What you'd actually do

  1. Leading and managing a team of network validation engineers responsible for end-to-end validation of NVIDIA's customer-facing networking solutions at scale, using a dedicated E2E simulation cluster environment.
  2. Defining the overall validation strategy and roadmap — establishing simulation methodologies, test coverage frameworks, and quality gates that align with product milestones and customer use cases.
  3. Pioneering the use of agentic AI flows within the validation organization — leading the team to design, build, and operate AI-driven agents capable of autonomously performing regression analysis, identifying coverage gaps, generating new test cases, and implementing validation code.
  4. Overseeing the design and continuous improvement of automated regression suites for networking protocols and large-scale simulation runs, ensuring scalable, repeatable, and high-confidence validation outcomes.
  5. Establishing a rigorous regression analysis culture — guiding the team in identifying trends, root causes, and systemic coverage gaps, and ensuring timely resolution in collaboration with engineering stakeholders.

Skills

Required

  • B.Sc. degree or equivalent experience in Computer Science, Electrical Engineering, Computer Engineering, or a related field.
  • 3+ years of experience in a leadership or management role, leading software or hardware validation/test engineering teams.
  • 7+ years of overall experience in network validation, network testing, or systems verification.
  • Proven track record of building and executing test automation strategies for network or distributed systems at scale, with hands-on background in Python-based automation.
  • Strong understanding of regression analysis methodologies — ability to drive actionable conclusions from large-scale test result datasets and translate them into engineering improvements.
  • Demonstrated ability to collaborate cross-functionally with architecture, design, and product teams in a fast-paced, multi-timezone environment.
  • Strong verbal and written communication skills, with experience presenting validation strategies and quality metrics to senior leadership.

Nice to have

  • Deep understanding of networking protocols and architectures (e.g., BGP, EVPN, VXLAN, RDMA/RoCE, Ethernet, IP routing, L2/L3 switching).
  • Experience with network simulation or emulation environments (e.g., Containerlab, GNS3, SONiC testbeds, or equivalent platforms) and the ability to guide teams in leveraging them effectively.
  • Experience managing validation of 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.
  • Prior experience with customer-facing solution validation or translating customer use cases into structured simulation and test scenarios.

What the JD emphasized

  • Pioneering the use of agentic AI flows
  • AI-driven agents capable of autonomously performing regression analysis
  • agentic workflows will continuously learn
  • test automation strategies
  • regression analysis methodologies

Other signals

  • Pioneering the use of agentic AI flows within the validation organization
  • leading the team to design, build, and operate AI-driven agents capable of autonomously performing regression analysis, identifying coverage gaps, generating new test cases, and implementing validation code
  • These agentic workflows will continuously learn from simulation results and product changes, dramatically accelerating the team's ability to scale test coverage and respond to emerging quality signals without manual intervention.