Join Microsoft Silicon, Cloud Hardware, and Infrastructure Engineering (SCHIE) to power Microsoft's 'Intelligent Cloud' mission. We are developing AI-native silicon and system-level solutions for next-generation AI models, combining custom accelerators, advanced networking, large-scale infrastructure, and cloud-scale software.

The Platform Systems Engineering (PSE) team seeks an AI Network HW Systems Engineer to architect, validate, optimize, and deploy networking infrastructure for our cutting-edge MAIA AI systems. This role offers a unique opportunity to shape the future of AI infrastructure at the intersection of networking hardware, systems architecture, and hyperscale deployment.

Collaborate across the entire networking stack, from high-speed interfaces and optics to switch silicon and AI communication frameworks. Work with diverse teams including silicon engineers, firmware developers, hardware designers, and Azure service teams to deliver scalable, reliable, and high-performance AI networking solutions.

Define and drive networking architectures for MAIA AI platforms, ensuring seamless integration and deployment from concept to datacenter. Lead the bring-up and qualification of network hardware technologies, including switches, NICs, PHYs, optics, and high-speed SerDes.

Develop and execute comprehensive validation strategies for AI networking infrastructure, focusing on functional, performance, scale, and reliability testing. Optimize AI fabric performance by analyzing latency, bandwidth utilization, and congestion behavior to translate workload requirements into scalable solutions.

Drive the evaluation and deployment of next-generation networking technologies like PAM4 SerDes, advanced optics, silicon photonics, and emerging innovations. Lead root-cause analysis for networking issues across all layers and develop telemetry, diagnostics, and automation solutions to enhance network reliability and engineering productivity.

This role requires deep expertise in AI infrastructure, including experience with GPU, FPGA, TPU, or AI accelerator-based systems, and familiarity with AI workload communication patterns and large-scale distributed AI training environments. A strong understanding of optical transceivers, DSP architectures, optical link budgets, and silicon photonics technologies is essential.

Experience with industry standards such as IEEE Ethernet, OIF specifications, and CMIS management frameworks is crucial. Familiarity with hyperscale datacenter deployments, ODM/CM/supplier ecosystems, and product lifecycle management (EVT, DVT, PVT, production ramps) is highly valued.

Proficiency in Linux environments and automation frameworks, coupled with experience in developing telemetry, diagnostics, validation, and qualification tooling, is necessary. Strong scripting and data analysis skills will further contribute to success in this role.