Principal AI Network HW Systems Engineer

Own AI networking architecture for MAIA training and inference systems across scale-up and scale-out deployments. Lead integration and qualification of switches, NICs, PHYs, optics, cables, and high-speed SerDes technologies. Define validation strategies covering functionality, interoperability, performance, scale, reliability, and stress testing. Optimize AI fabric performance through analysis of latency, bandwidth utilization, congestion management, and collective communication efficiency. Drive debugging, telemetry, and automation solutions that improve network resiliency and operational excellence. 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 developing GPU, FPGA, TPU, AI accelerator, or HPC-based systems. Familiarity with AI workload communication patterns and collective operations. Understanding of large-scale distributed AI training environments. Optical transceivers (DR4, DR8, FR4) DSP architectures TIAs and drivers Optical link budgets OMA, TDECQ, receiver sensitivity analysis Silicon photonics technologies Co-packaged optics architectures IEEE Ethernet standards OIF specifications CMIS management frameworks Ultra Ethernet Consortium technologies Future 224G and 448G ecosystems Experience with hyperscale datacenter deployments. Experience managing products through EVT, DVT, PVT, and production ramps. Experience with Linux environments and automation frameworks.