Principal AI Systems Architect

Define AI rack- and cluster-scale architecture across accelerator, host, network, memory, storage, power, cooling, and datacenter constraints. Make quantitative tradeoffs across performance and TCO. Develop functional proof-of-concepts to evaluate and de-risk new capabilities Write and own high-level product requirements and specifications Drive alignment across cross-functional engineering and leadership teams. Own the outcomes. Use performance simulations and hardware models for what-if studies to evaluate and choose between architecture options. Industry influence: Participate in industry consortiums to shape standards, and influence vendor roadmaps. Master's Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 9+ years technical engineering experience OR Bachelor's Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 11+ years technical engineering experience OR equivalent experience Doctorate in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 8+ years technical engineering experience OR Master's Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 12+ years technical engineering experience OR Bachelor's Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 15+ years technical engineering experience OR equivalent experience Experience in large scale AI datacenter systems and their network Demonstrated expertise in one or more of these domains: networking (RoCE, Infiniband transports, congestion control, network topology design), reliability and serviceability, datacenter architecture for large scale AI systems Experience owning hardware system architecture at scale - machine, rack row and cluster, authoring specifications and requirements Ability to make decision trade-offs across domains Deep expertise in AI systems or a key domain such as networking, reliability, power management Skilled in partnering and influencing architects, hardware engineers, and software leads Ability to manage through ambiguity, bringing clarity to establish direction and driving decisions Collaboration skills, teamwork, and sense of presumed responsibility