Principal AI Network Hardware Systems Engineer

The Platform Systems Engineering (PSE) team is seeking a Principal AI Network Hardware Systems Engineer to lead the architecture, bring-up, validation, optimization, and deployment of networking infrastructure for Microsoft's MAIA AI platform. This role combines networking hardware, systems architecture, AI infrastructure, and large-scale deployment to deliver industry-leading AI performance and reliability. You will work across the networking stack, spanning high-speed SerDes, optics, cables, NICs, PHYs, switch silicon, AI communication frameworks, and distributed training systems.

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.

Lead design and validation of IP-based AI networking solutions spanning TCP/IP, UDP, routing, congestion management, flow control, QoS, and traffic engineering.

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.

Drive optimization of network communication paths supporting distributed AI training and inference.

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.

Develop and execute networking validation strategies covering functionality, performance, scale, interoperability, resiliency, and reliability.

Characterize network behavior under AI training and inference workloads.

Evaluate latency, bandwidth utilization, congestion events, flow distribution, and workload communication patterns.

Create and automate network stress, scale, and performance qualification methodologies.

Lead end-to-end troubleshooting of networking issues across physical, data link, network, and transport layers.

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.

Drive corrective actions and long-term reliability improvements using fleet telemetry and lab validation.

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.