Principal HPC Network Engineer

Hybrid in Republic of Lithuania, Latvia

Infrastructure Automation and Orchestration

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We are seeking a Principal HPC Network Engineer to support advanced AI, research, and Kubernetes-based GPU infrastructure for a major global technology client.

An ideal candidate is an ownership-driven, hands-on network expert who combines deep low-level troubleshooting with architectural thinking and clear communication in complex AI/HPC, Kubernetes, and GPU infrastructure environments.

Responsibilities

Architect, operate and troubleshoot high-performance InfiniBand/RDMA and Ethernet fabrics for large-scale GPU clusters and distributed AI/LLM workloads
Design and evaluate cluster network topologies, including Fat-tree, Clos, Rail-optimized and Dragonfly, based on workload scale and performance needs
Optimization of host-side networking, including NIC configuration, drivers, firmware, IRQ affinity, NUMA placement, PCIe topology and GPU-to-NIC communication paths
Tune and troubleshoot RDMA/RoCE, NCCL/MSCCL and collective communication performance for multi-node GPU training workloads
Design and maintain Kubernetes networking for GPU clusters, including CNI plugins, network policies, multi-NIC pods, RDMA/GPU device plugins and workload orchestration integration
Support for SmartNIC/DPU technologies such as NVIDIA BlueField where applicable, including SR-IOV, offload, isolation and security use cases
Build and improve network observability, including metrics, dashboards, alerts, congestion detection, latency tracing, SLO reporting and capacity/performance analysis
Collaboration with Kubernetes, storage, GPU infrastructure, observability and AI research teams to resolve network and I/O bottlenecks and improve workload reliability

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
Expert-level experience in high-performance networking for HPC, AI/ML, GPU clusters or large-scale compute environments
Knowledge of InfiniBand fabrics, including NDR/HDR or comparable high-speed generations, subnet managers, fabric configuration, topology and troubleshooting
Understanding of RDMA networking concepts, including InfiniBand, RoCE/RoCEv2, GPUDirect-related patterns, congestion behavior and performance tuning
Skills in Kubernetes and container networking for GPU or distributed workloads, including CNI concepts, network policies, multi-NIC patterns and RDMA/GPU device integration
Proficiency in Linux networking and host-side troubleshooting, including NIC configuration, drivers, firmware, IRQ affinity, NUMA awareness, PCIe topology, MTU, offloads and performance diagnostics
Expertise in network observability and performance management, including telemetry, traffic monitoring, congestion detection, latency analysis, SLOs, capacity planning, alerting and troubleshooting across L1-L4, fabric and RDMA layers
Practical knowledge of distributed AI training communication patterns, including NCCL-based workloads and collective operations such as all-reduce and all-gather
Familiarity with host-side networking, including NICs, PCIe topology, NUMA awareness and GPU-to-NIC affinity
Capability to perform troubleshooting, root-cause analysis, documentation and communication with client engineering teams, researchers and platform stakeholders

Nice to have

Knowledge of Azure Networking, Ethernet and GPGPU/GPU
Skills in Grafana, Prometheus and Network Administration
Proficiency in Python and UNIX shell scripting
Capability to perform Infrastructure as Code development and maintenance