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Normal Computing is an applied AI company building foundational hardware and software for the semiconductor industry and critical AI infrastructure. As a Hardware System Design Engineer, you will lead the end-to-end system architecture design of Normal's AI compute hardware platforms, from initial concept through specification, design reviews, integration, and production deployment.
You will own the complete hardware system architecture lifecycle, translating workload and performance requirements into hardware decisions. Your responsibilities span boards, chassis, interconnects, and rack-scale system design, including system topology, power delivery, thermal management, scale-up and scale-out fabrics, and networking. You'll work closely with hardware, software, and cross-functional engineering teams to ensure designs meet performance and workload requirements.
Key responsibilities include defining system requirements and specifications, evaluating architectural options, and driving critical technical decisions as hardware platforms evolve. You'll assess partner and vendor designs against requirements, identify technical gaps and schedule risks early, and lead system-level tradeoffs. You'll own the bring-up, system integration, validation, and qualification strategy, playing a central role in bringing disparate components into cohesive, high-performance AI compute systems from architecture through production deployment.
You should have significant experience architecting complex compute hardware systems spanning server or machine-level designs through rack-scale platforms. Experience taking hardware systems from early requirements and architecture through specification, integration, bring-up, validation, and production deployment is essential. You'll work with ODMs, suppliers, and hardware partners, reviewing designs, defining technical requirements, and driving technical issues to resolution. A degree in Electrical Engineering, Computer Engineering, or related field is required. Experience designing high-performance AI compute systems at server and rack scale, or bringing novel hardware architectures into production-scale systems, is a plus.
