QA E2E Engineer

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.

What You'll Do:

• Design, create and execute comprehensive end-to-end testing scenarios for ACO based on detailed requirements and design documents.
• Test 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.
• Maintain and ensure functionality across the QA environments: AI fabric topologies (leaf-spine, DNOS and multi-vendor switches), GPU servers, NetBox/SLURM/Kubernetes integrations and the ACO deployments.
• 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 existing AI/HPC benchmarks (RDMA, NCCL/RCCL, MPI, RoCE, training/inference, storage) on GPU and CPU clusters through ACO's benchmarking engine; compare actual performance against established baselines.
• Review and understand benchmarking methodologies and test procedures for high-performance networking and collective communication technologies.
• Support telemetry collection and validate test data (gNMI/OpenConfig counters, syslog, GPU and NIC metrics) for accuracy, consistency and reliability against device state.
• Use performance analysis tools, benchmarking frameworks and diagnostic utilities to execute tests, analyze results and troubleshoot issues.
• Execute regression tests to verify that software updates, configuration changes and platform upgrades do not introduce new issues.
• Collaborate closely with development teams to investigate failures, reproduce defects with clear evidence, verify fixes and ensure system quality.
• Document test procedures, execution results, defects and performance metrics to maintain traceability and reproducibility; prepare clear QA reports highlighting deviations, failures and potential issues.
• Use AI agents in your daily work to accelerate test design, execution, evidence collection and reporting, and help grow the team's test automation.

Requirements

What We're Looking For:

• 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 (STP/STD/STR).
• 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).
• Agile Experience: hands-on experience in Agile work processes.

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:

• Excellent interpersonal and communication skills.
• 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?

• Be part of the team building 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!