Chief HPC Network Engineer - AI Infrastructure

Description

We are looking for a **Chief HPC Network Engineer** to define the global technical strategy, reference architecture, and engineering vision behind advanced AI, research, and Kubernetes\-based GPU infrastructure for a major global technology client. The role focuses on establishing the long\-term technical direction, governing architecture decisions across multiple programs, and setting organization\-wide engineering standards for high\-performance network fabrics supporting massive\-scale LLM and distributed AI workloads, including InfiniBand/RDMA, high\-speed Ethernet, Kubernetes networking, host\-side GPU networking, SmartNIC/DPU technologies, and deep network observability. As a principal technical authority, you will shape engineering culture, mentor lead and principal engineers, influence executive client roadmaps, and own end\-to\-end governance of mission\-critical network platforms across the portfolio. The ideal candidate combines authoritative expertise across InfiniBand NDR/HDR and next\-generation fabrics, RDMA/RoCE, NVIDIA/Mellanox networking, NCCL/MSCCL communication patterns, Linux host networking, PCIe/GPU/NIC topology, and Kubernetes networking for GPU clusters, with a proven track record of leading multiple engineering teams, defining technical strategy at the program level, and shaping industry\-leading HPC/AI network platforms. **Responsibilities** * Define and own the multi\-year strategic vision and architectural roadmap for high\-performance InfiniBand/RDMA and Ethernet fabrics powering massive\-scale GPU clusters and distributed AI/LLM workloads across the client portfolio * Govern the design, evaluation, and standardization of cluster network topologies, including Fat\-tree, Clos, Rail\-optimized, and Dragonfly, and establish enterprise\-wide decision frameworks aligned with workload scale, performance, and cost constraints * Establish and enforce organization\-wide engineering standards and best practices for host\-side networking, including NIC configuration, drivers, firmware, IRQ affinity, NUMA placement, PCIe topology, and GPU\-to\-NIC communication paths * Set the strategic direction for performance engineering across RDMA/RoCE, NCCL/MSCCL, and collective communication for multi\-node GPU training workloads, and oversee resolution of the most complex systemic performance issues * Define the canonical reference architecture for Kubernetes networking on GPU clusters, including CNI plugins, network policies, multi\-NIC pods, RDMA/GPU device plugins, and workload orchestration integration, and drive its adoption across programs * Own the strategy and governance for SmartNIC/DPU technologies such as NVIDIA BlueField, including SR\-IOV, offload, isolation, and security use cases, and align adoption with the broader infrastructure roadmap * Define the enterprise observability strategy for network platforms, governing metrics, dashboards, alerts, congestion detection, latency tracing, SLO frameworks, and capacity/performance analysis methodologies * Provide technical leadership and mentorship to lead and principal engineers across network, Kubernetes, storage, GPU infrastructure, observability, and AI research teams, building the talent pipeline and driving cross\-functional alignment at scale * Act as the principal technical authority in executive client and stakeholder forums, shaping strategic technical direction, negotiating trade\-offs at the program level, and ensuring delivery of reliable, scalable network platforms across multiple engagements * Contribute to the broader engineering community through thought leadership, internal practice development, and representation of the company at industry events **Requirements** * 8\+ years of experience in network, infrastructure, HPC, SRE, or similar engineering roles, with 4\+ years focused on HPC, AI/ML, or GPU cluster networking, including demonstrated technical leadership at the program or portfolio level (2\+ years) * Proven experience defining the architecture and governing delivery of InfiniBand/RDMA fabrics, high\-speed Ethernet, and Linux networking in large\-scale, performance\-critical distributed compute environments * Authoritative expertise in host\-side networking, including NICs, drivers, and firmware, along with PCIe topology, NUMA awareness, and GPU\-to\-NIC affinity, with proven ability to set enterprise\-wide standards and uplift engineering organizations * Deep understanding of distributed AI training communication patterns, including NCCL\-based workloads and collective operations such as all\-reduce and all\-gather, with the ability to drive workload\-network co\-design strategy at scale * Authoritative knowledge of Kubernetes and container networking for GPU or distributed workloads, including CNI concepts, network policies, multi\-NIC patterns, and RDMA/GPU device integration, with experience defining reference architectures * Expert\-level mastery of RDMA networking concepts, including InfiniBand, RoCE/RoCEv2, GPUDirect\-related patterns, congestion behavior, and performance tuning at very large scale * Mastery of Linux networking and host\-side troubleshooting, including IRQ affinity, MTU, offloads, and performance diagnostics, with the ability to define diagnostic methodologies for the broader engineering organization * Demonstrated ownership of enterprise network observability and performance management strategy, including telemetry, traffic monitoring, congestion detection, latency analysis, SLOs, capacity planning, and alerting/troubleshooting across L1\-L4, fabric, and RDMA layers * Outstanding leadership, mentoring, stakeholder management, and executive communication skills, with proven experience leading multiple engineering teams, influencing C\-level client architecture decisions, and driving consensus across researchers, platform stakeholders, and executive sponsors * English language proficiency at an Advanced level (C1\) **Nice to have** * Hands\-on architectural and strategic experience with Azure Networking, Ethernet, and GPGPU/GPU technologies * Authoritative command of Grafana, Prometheus, and Network Administration, with experience defining observability standards across an engineering organization * Proven ability to define strategy, govern, and scale Infrastructure as Code practices across multiple teams and programs * Proficiency in Python and UNIX shell scripting for automation, tooling, and enabling organization\-wide engineering productivity * Track record of thought leadership through conference talks, publications, patents, or open\-source contributions in the HPC/AI networking domain