AI Fabric - QA SIT Team Leader

Description

Location: Ra'anana

#Hybrid

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.

Responsibilities:

• Build, lead and mentor the DNOS-SAI SIT team; own the quality of DNOS-SAI releases on Broadcom Tomahawk (TH5/TH6) platforms.
• Learn, investigate and write test plans for AI data-center use cases: leaf-spine and rail-optimized Clos fabrics, lossless Ethernet (PFC/ECN/DCQCN), ECMP, eBGP underlay, EVPN/VXLAN and multi-tenancy.
• Simulate and validate the integration between routing, switching and QoS features in complex environments: BGP, EVPN/VXLAN, ECMP, QoS/PFC/ECN, LACP, BFD, LLDP, ACLs, telemetry, ZTP/upgrades, port breakout and 400G/800G/1.6T optics.
• Run testing scripts and traffic scenarios in the lab (Spirent/Ixia, RDMA and collective traffic) and analyze counters, drops, latency and congestion at the switch and at the GPU NIC.
• Validate GPU clusters and AI workloads end to end: RoCEv2/RDMA performance, NCCL/RCCL benchmarks and training/inference jobs under SLURM or Kubernetes.
• Lead defect investigation with R&D (reproduce, isolate ASIC/SAI vs DNOS behavior, verify fixes, run regression) and be an integrated part of network design and bring-up for our AI customers.

Requirements

• 3+ years leading a QA/SIT team on top of 5+ years of hands-on system/integration testing of networking products.
• Deep knowledge and experience in data-center routing and switching: BGP, EVPN/VXLAN, ECMP, VLANs, IP addressing, MTU and L2/L3 troubleshooting.
• AI networking: hands-on experience with AI/HPC fabrics – RoCEv2/RDMA, lossless Ethernet (PFC/ECN/DCQCN), rail-optimized Clos designs, 400G/800G/1.6T optics.
• GPU clusters and AI workloads: running and monitoring NCCL/RCCL, RDMA and MPI benchmarks and training/inference jobs on NVIDIA/AMD GPU servers (SLURM or Kubernetes).
• Knowledge and experience with Linux systems.
• Knowledge with services such as LLDP, LACP, BFD, SNMP, TACACS/RADIUS, QoS and gNMI telemetry.
• Hands-on use of AI agents in daily engineering work (skills, rules files, MCP tools; e.g., Claude Code, Codex, Cursor).
• Able to work in complex environment/tasks; self-learning and issue solving.
• Good communication skills, worldwide customer facing, good English.

Advantage:

• CCNP, CCIE certificate or equivalent knowledge.
• Experience with white-box switches and SAI/SONiC-based network operating systems on Broadcom Tomahawk ASICs.
• Service-provider technologies such as IS-IS/OSPF, MPLS/Segment Routing, L3VPN, SR-TE and multicast (PIM, MVPN).
• Testing tools such as Ixia or Spirent, and RDMA/collective tools (perftest, NCCL/RCCL tests, OSU benchmarks).
• Automation scripts in Python, Bash or Java and CI pipelines; Kubernetes, containers and cloud deployments (AWS/Azure/GCP).
• QA methodologies (HLD/LLD-based test plans) and tools such as Jira and TestRail.