High Performance Network Engineer AI Inference Network Interface Card

You will optimize NIC, PCIe, driver, kernel, and RDMA communication performance across single-host and multi-host AI inference clusters. You will improve critical inference communication paths, including KV cache transfer, cross-node collective communication, and model-weight loading. You will investigate packet loss, latency variation, slow nodes, and NIC failures; strengthen monitoring and automated diagnostics; collaborate with inference-framework, hardware, and NIC-vendor partners; and evaluate emerging networking technologies.

Responsibilities

• Optimize NIC, PCIe, driver, kernel, and RDMA communication performance
• Improve KV cache transfer, cross-node collective communication, and model-weight loading paths
• Investigate packet loss, latency variation, slow nodes, and NIC failures
• Analyze root causes across NICs, PCIe, drivers, kernels, and applications
• Build monitoring, alerting, automated diagnostics, and performance baselines
• Collaborate with inference-framework, hardware, and NIC-vendor partners
• Evaluate and implement emerging NIC, RDMA, and GPU-interconnect technologies

Requirements

• 3+ years of relevant experience
• Knowledge of computer networking and operating systems
• Knowledge of RDMA, including QP, Verbs, memory registration, and transport modes
• Experience with RoCEv2 or InfiniBand
• Knowledge of collective communication algorithms including Ring, Tree, Halving-Doubling, and All2All
• Knowledge of Linux networking and the effects of PCIe and NUMA on communication performance
• Ability to independently perform performance tuning and fault troubleshooting
• Proficiency in C or C++ and at least one of Python, Go, or Shell