QA E2E Team Leader

Description

Location: Ra'anana

DriveNets is a leader in high-scale disaggregated networking solutions. Founded in 2015, DriveNets modernizes the way service providers, cloud providers and hyperscalers build networks. Supporting the largest network in the world, more than half of AT&T’s backbone traffic is running on DriveNets’ Network Cloud open disaggregated architecture. Raising $587 million in three funding rounds, DriveNets is disrupting the networking market from high-scale architecture to AI platforms, and is bringing onboard the most talented people. We are seeking people that want to make an impact on the world’s leading communication networks and are experienced in networking architecture or AI infrastructure solutions.

Job Summary:

What You'll Do:

• Build, lead and mentor the ACO E2E QA team; own the quality of every ACO release from test planning to Go/No-Go sign-off.
• Define the E2E test strategy, test plans (STP/STD/STR) and acceptance criteria for ACO features based on requirements and design documents, and present them to R&D and Product.
• Lead manual end-to-end testing of ACO across its UI/UX, REST APIs, database-backed workflows and the real infrastructure it manages: switch provisioning and upgrades, inventory and topology, telemetry, alerts, configuration compliance, benchmarking and multi-tenancy.
• Own and maintain the QA lab environments: AI fabric topologies (leaf-spine, DNOS and multi-vendor switches), GPU servers, NetBox/SLURM/Kubernetes integrations and the ACO deployments themselves (single server, HA cluster, cloud).
• Validate network connectivity, configuration and performance across the AI cluster infrastructure: high-speed Ethernet (400/800G/1.6T), GPU nodes, leaf-spine topologies, VLAN/VXLAN, IP addressing, MTU, link status, routing and packet loss.
• Verify BGP routing functionality (neighbor establishment, route advertisement, route propagation and convergence) and EVPN/VXLAN-based isolation on data-center fabrics.
• Execute and analyze AI/HPC benchmarks (RDMA, NCCL/RCCL, MPI, RoCE, training/inference, storage) on GPU and CPU clusters through ACO's benchmarking engine; compare results against established baselines and report deviations.
• Validate telemetry collection (gNMI/OpenConfig, syslog, GPU and NIC exporters) for accuracy, consistency and reliability against device state and live traffic.
• Drive the defect lifecycle: reproduction with evidence, prioritization, root-cause investigation with developers, fix verification and regression; publish clear quality reports and release-readiness metrics.
• Define and execute the automation roadmap, moving the team from manual E2E testing to automated API, UI and device-level test suites integrated into CI.
• Make AI agents part of the team's daily work (test design, execution, evidence collection, reporting) and set the practices for AI-assisted QA.
• Represent QA in planning, agile ceremonies, customer escalations and field deployments.

Requirements

What We're Looking For:

• 3+ years of experience leading QA engineers (hiring, mentoring, planning, delivery) on top of 5+ years of hands-on QA.
• Minimum 3 years of application testing across UI/UX, database-backed data and multi-step workflows in web-based systems, including test design from detailed requirements.
• Networking knowledge at CCNP level or higher: L2/L3 switching and routing, BGP, VLAN/VXLAN/EVPN, MTU/MSS, ECMP, and troubleshooting with packet captures and device CLI.
• AI networking: data-center fabrics for AI workloads - leaf-spine and rail-optimized Clos designs, RoCEv2/RDMA, lossless Ethernet (PFC/ECN/DCQCN), 400/800G/1.6T optics and link health.
• GPU clusters: running, monitoring and optimizing AI workloads and benchmarks (NCCL/RCCL, RDMA, MPI, training and inference) on NVIDIA/AMD GPU servers, with schedulers such as SLURM or Kubernetes.
• Strong hands-on experience in Linux environments (CLI, networking stack, services, logs, scripting).
• Hands-on experience working with AI agents in daily engineering work: agent skills, rules/instruction (markdown) files, MCP servers and tools, and prompt-driven workflows (e.g., Claude Code, Codex, Cursor).

Advantages:

• QA automation experience (Python, pytest, Playwright/Selenium, REST API testing, CI pipelines).
• Kubernetes and containers (kubectl, Helm, Lens/OpenLens, Argo CD; monitoring pod status, reading logs, troubleshooting).
• Cloud deployments (AWS/Azure/GCP, e.g., EKS).
• Databases: SQL querying and data validation (PostgreSQL).
• Familiarity with network operating systems (DNOS, SONiC, Arista EOS, Junos), observability stacks (Prometheus/Mimir, Grafana/Perses, gNMI) and traffic generators (Spirent/Ixia).

Soft Skills:

• Proven leadership: builds a team, sets priorities, makes decisions and owns outcomes.
• Excellent interpersonal and communication skills with R&D, Product, Support and customers.
• High-level proficiency in English (both written and verbal) and clear technical writing.
• Self-starter who learns quickly and adapts easily to new technologies.
• Ability to work under tight deadlines and manage multiple priorities.
• A strong team player with a collaborative mindset.

Why Join Us?

• Build the QA team of a new product - DriveNets' AI Cluster Orchestrator - used to deploy and run some of the most demanding AI infrastructures.
• Work hands-on with state-of-the-art AI infrastructure: 800G/1.6T Ethernet fabrics, NVIDIA and AMD GPU clusters, RoCE and collective communication.
• Join an AI-native engineering culture where agents are part of the daily workflow.
• Collaborate with brilliant minds in a dynamic and supportive work environment.
• If you're ready to take the next step in your career and work with a world-class team, apply now!