Principal AI Network Hardware Systems Engineer

AI Network Architecture & System Integration Define and develop networking requirements for large-scale AI training and inference clusters. Collaborate with silicon, system software, firmware, hardware, and Azure infrastructure teams to deliver scalable networking solutions from concept through datacenter deployment. Participate in architecture reviews and influence next-generation AI networking roadmaps. Define network concepts of operation, serviceability requirements, telemetry requirements, and operational models for AI infrastructure. Analyze transport-layer behavior and performance characteristics across large-scale distributed AI workloads. Evaluate network protocol implementations and debug issues impacting latency, throughput, scalability, and reliability. Design, validate, and optimize RDMA-based networking solutions for AI clusters. Analyze RDMA performance, congestion behavior, packet loss, retransmissions, and collective communication efficiency. Work closely with networking vendors and software teams to optimize AI fabric performance and workload scalability. Develop validation methodologies for AI traffic patterns and collective communication workloads. Performance Characterization & Validation Develop and execute networking validation strategies covering functionality, performance, scale, interoperability, resiliency, and reliability. Evaluate latency, bandwidth utilization, congestion events, flow distribution, and workload communication patterns. Perform packet-level analysis and protocol debugging using telemetry, packet captures, performance counters, and diagnostic tools. Investigate network switch, NIC, RDMA, routing, congestion control, and protocol-related issues. Build and improve network observability, diagnostics, telemetry, and monitoring solutions. Develop tools and automation for network validation, performance analysis, and failure detection. Improve engineering productivity through automated testing, qualification, and network health assessment frameworks. Master's Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 7+ years technical engineering experience OR Bachelor's Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 8+ years technical engineering experience OR equivalent experience 8+ years of experience in NW HW development 8+ years of experience in GPU based SU/SO development 8+ years of hands on experience with HS interface architecture and development These requirements include but are not limited to the following specialized security screenings: Experience with RDMA technologies, AI fabrics, and distributed training environments. Understanding of RoCE, congestion control, ECN, PFC, DCQCN, and related AI networking technologies.