AI Networking Architecture & System Development Develop and integrate scale-up and scale-out networking solutions for MAIA AI training and inference systems. Translate AI/ML workload requirements into system-level networking requirements. Evaluate architecture tradeoffs across performance, reliability, power, scalability, serviceability, and cost. Collaborate across silicon, firmware, hardware, software, and OS teams to resolve complex integration issues. Develop end-to-end validation strategies covering functionality, performance, scale, interoperability, reliability, and stress. Develop validation methodologies for rack-scale and cluster-scale AI infrastructure. Evaluate technologies such as PAM4 SerDes, DAC/AEC, OSFP/QSFP-DD, advanced optical interconnects, LPO/LRO, CPO, and silicon photonics. Analyze system-level tradeoffs across performance, power, reliability, signal/link integrity, and scalability. Develop and improve telemetry, diagnostics, validation automation, and network health monitoring. Master's Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 3+ years technical engineering experience OR Bachelor's Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 5+ years technical engineering experience OR equivalent experience. 5+ years of experience developing or validating networking, compute, storage, or accelerator hardware systems. 5+ years of experience designing, integrating, validating, or troubleshooting Ethernet-based networking infrastructure, including switches, NICs, optics, PHYs, or high-speed SerDes technologies. 5+ years of experience supporting AI, HPC, cloud, or large-scale data center infrastructure deployments. These requirements include but are not limited to the following specialized security screenings: Experience with GPU, FPGA, TPU, or custom AI accelerator systems supporting AI/ML workloads. Experience with scale-up and scale-out AI networking and distributed AI infrastructure. Electrical networking expertise in areas such as 112G/224G SerDes, PAM4, MAC/PCS, FEC, link training, signal integrity, DACs, and AECs, or optical expertise in areas such as OSFP/QSFP-DD, AOCs, optical transceivers, optical link characterization, and diagnostics. Familiarity with optical technologies such as DR4/DR8/FR4, optical link budgets, OMA, TDECQ, receiver sensitivity, LPO, LRO, CPO, or silicon photonics. Understanding of AI workload communication patterns, network collectives, traffic profiles, and congestion behavior. Familiarity with IEEE Ethernet, OIF, CMIS, and emerging AI networking technologies. Experience with Linux, telemetry, diagnostics, scripting, test automation, and network qualification. Experience with hyperscale datacenter infrastructure and hardware development from prototype through production.