What youll be doing:
Design and review test and product requirements across the Ethernet / NIC / DPU / Switch portfolio, focusing on large‑scale AI cluster behavior.
Build and maintain realistic customer‑like testbeds, including heterogeneous hardware, OS / driver combinations and complex network fabrics.
Own end‑to‑end cluster troubleshooting: reproduce customer scenarios, triage across the stack and drive issues to root cause and fix.
Read and understand relevant source code to identify defects, validate fixes and improve logging and instrumentation.
Collaborate closely with development teams to debug NCCL, RoCE/RDMA and related networking components using logs, code inspection and targeted experiments.
Define tests and guide the automation team to implement robust suites that produce actionable logs, metrics and traces.
Run Regression, Performance, Functional and Scale testing, analyze results and provide clear, data‑driven reports to stakeholders.
Profile and benchmark deep learning training and inference workloads, correlating model‑level metrics with system and network telemetry to uncover bottlenecks.
What we need to see:
B.A./B.Sc. in Computer Science, Electrical Engineering, or equivalent IT/Network/Systems experience.
5+ years of hands‑on networking or system‑level testing and debugging on Linux.
Strong Linux networking and debugging skills (for example perf, tcpdump, ethtool, iproute2).
Proven production‑grade debugging experience: forming hypotheses, running experiments, and driving issues to root cause under pressure.
Expertise in host‑side NIC validation and tuning (offloads, queues, interrupts, firmware/driver interactions).
Strong knowledge of AI networking libraries (such as NCCL) and protocols (such as RoCE and RDMA), including performance and correctness debugging.
Ability to read and reason about source code (C/C++/Python or similar) and collaborate closely with developers on fixes.
Solid scripting and automation skills with Bash / Python / Ansible for setup, log collection, and experiment orchestration.
Fast learner, familiar with modern AI tools and workflows, able to adapt quickly.
Excellent analytical, problem‑solving and communication skills, with strong ownership and a collaborative mindset.
Ways to stand out from the crowd:
Hands‑on debugging of collective communication libraries (for example NCCL) or large‑scale LLM training / inference clusters.
Experience with large cluster environments (tens to thousands of GPUs or nodes), including incident response and post‑mortem analysis.
Deep expertise in tuning and debugging congestion control and lossless Ethernet for AI workloads (for example DCQCN, ECN, PFC).
Familiarity with NVIDIA networking technologies (for example BlueField / BF3, ConnectX NICs) and their software stack and diagnostics.
Experience debugging issues that span multiple layers (L2/L3, transport, AI frameworks) or contributing to open‑source networking / AI systems.
