Principal AI Back-End Network Systems Lead

End-to-End Product Ownership & Architecture Own the Back End Network product definition and execution plan across scale-up and scale-out, from customer/workload needs and product requirements through architecture, development, qualification, deployment, and fleet learning. Translate AI training and inference requirements into measurable system targets for bandwidth, latency, scale, collective efficiency, reliability, availability, serviceability, power, thermal behavior, cost, security, and schedule. Make and drive cross-system architecture decisions, interface contracts, feature priorities, and risk tradeoffs; maintain clear ownership, milestones, entry/exit criteria, and product readiness metrics. Lead NUDD (new, unique, different, and difficult) technology assessments, prototypes, make/buy/partner decisions, and mitigation plans for the highest product risks. Ensure hardware, firmware, drivers, network operating systems, management software, orchestration, and datacenter operations are aligned to common specifications and concepts of operation. Establish analytical and simulation models for topology, bandwidth, latency, congestion, failure behavior, collective efficiency, workload scaling, power, and cost. Create clarity and technical alignment across architecture, silicon, hardware, optics, firmware, networking, software, validation, manufacturing, datacenter, and workload teams. Influence internal roadmaps and external partners, including silicon, switch, optical, interconnect, ODM/OEM, and open-source ecosystems; resolve cross-team gaps and ambiguous ownership. Communicate product status, architectural decisions, risks, tradeoffs, and executive asks clearly while mentoring engineers and raising the quality of end-to-end systems engineering. 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 Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, or a related technical field and 10+ years of relevant experience in networking, distributed systems, accelerator platforms, or cloud infrastructure or equivalent experience. 6+ years of experience designing, developing, integrating, or validating networking for AI/ML, HPC, accelerator, or large-scale cloud systems. Deep hands-on expertise in at least one AI/ML networking domain, such as scale-up interconnects and silicon/SerDes/PHY, electrical or optical connectivity, switch systems, scale-out Ethernet/RDMA fabrics, transport/congestion control, collective communication, or workload/fabric performance. Demonstrated experience leading a complex hardware/software product or subsystem through requirements, architecture, development, integration, validation, and production deployment.

architecting or delivering both scale-up and scale-out networks for large AI training or inference systems.

with high-speed electrical and optical technologies, SerDes/PHY, FEC, signal integrity, link training, retimers, cable/backplane design, pluggable or co-packaged optics, and 400G/800G or faster links.

with switch ASICs and systems, NICs/DPUs, Ethernet, RDMA/RoCE, routing, QoS, ECN/PFC/DCQCN or related congestion-control methods, SONiC/Linux networking, and network operating systems.

Understanding of collective communication libraries and operations, distributed training parallelism, AI workload traffic patterns, and workload-driven network optimization.

with network and system modeling, simulation, emulation, performance analysis, topology studies, traffic generation, or collective/workload scaling analysis.

developing automated validation, stress, reliability, interoperability, telemetry, diagnostics, observability, and fleet-health frameworks.

Track record of system bring-up and cross-layer debug from physical links through protocol, runtime, collectives, and application behavior.

driving manufacturing readiness, supplier/partner execution, datacenter deployment, serviceability, and fleet operations for hardware-intensive products.

Strong product judgement, technical leadership, collaboration, written communication, and executive presentation skills.