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Manager, AI Networking Performance Research and Analysis
·Israel
Not specifiedFull-timeProduct ManagementComputer Hardware Manufacturing
Lead end-to-end performance strategy for next-generation networking technologies, including NICs, switches, and AI clusters. Drive benchmarking, telemetry, and optimization across hardware and software platforms at a leading computer hardware company.
Responsibilities
- Lead performance research and evaluation of advanced networking technologies for AI workloads, including LLM training and inference at supercomputing scale.
- Define end-to-end performance test plans and methodology for next-generation networking hardware and technologies, including performance expectations and target KPIs.
- Drive benchmarking, profiling, reporting, and deep performance characterization of networking workloads and offload features.
- Collaborate closely with simulation, architecture, chip-design, firmware, and software teams to assess performance tradeoffs and identify bottlenecks.
- Perform deep root cause analysis for performance gaps and stability issues, and drive cross-team mitigation plans.
- Develop and enhance performance analysis tools, automation frameworks, and scalable methodologies for cluster-level performance evaluation.
- Own performance observability efforts, including telemetry pipelines, dashboards, and job-level performance analytics.
Requirements
- B.Sc in Computer Science or Software Engineering.
- 5+ years of experience with high-performance networking technologies (RDMA, Storage, Security, OVS, MPI).
- 3+ years as an engineering team manager.
- Demonstrated performance analysis skills and methodologies.
- Experience with cluster-level performance, telemetry, NICs, DPUs, switches, and GPUs.
- Fast and self-learning capabilities with strong analytical and problem-solving skills.
- Proficiency in Python, Bash, and C/C++.
- Experience with Linux OS distributions.
- Strong communication and interpersonal skills.
Nice to have
- Deep system-level architecture knowledge (Intel/AMD/ARM CPUs, NVIDIA GPUs, HCA/DPU architecture, memory subsystems, PCIe, storage, NVLink).
- Strong expertise in RDMA networking performance and AI communication stacks (e.g., NCCL).
- Proven experience analyzing AI workload communication patterns and benchmarking distributed LLM training workloads at scale.
- Experience designing telemetry frameworks, monitoring pipelines, and performance dashboards for large clusters.
- Familiarity with modern AI tooling including performance-driven agents, automation pipelines, and RAG-based applications.