Hardware Engineer, Normal Computing
System Design
$200-400k
+ equity
Generative AI for enterprise
Be an early applicant
Generative AI for enterprise
21-100 employees
Be an early applicant
$200-400k
+ equity
21-100 employees
Company mission
To bridge artificial intelligence to the most sensitive industrial and advanced manufacturing applications around the globe.
Role
Who you are
- 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
- Experience working with ODMs, suppliers, and other hardware partners, including reviewing designs, defining technical requirements, and driving technical issues to resolution
- Degree in Electrical Engineering, Computer Engineering, or a related field
Desirable
- Experience designing high-performance AI compute systems at server and rack scale
- Experience bringing novel hardware architectures into production-scale compute systems
- Advanced degree in Electrical Engineering, Computer Engineering, or a related field
What the job involves
- As a Hardware System Design Engineer, you will lead the system architecture design of Normal’s AI compute hardware end-to-end, from initial concept to specification, design reviews, integration, and deployment. You will guide system-level decisions spanning boards, chassis, interconnects, and rack-scale system design, collaborating closely across hardware, software, and other engineering teams to ensure system designs meet performance and workload requirements
- You’ll define system requirements and specifications, evaluate architectural options, and drive key technical decisions as our hardware platforms evolve. You’ll evaluate partner and vendor designs against our requirements, identify technical gaps and schedule risks early, and lead system-level tradeoffs. You’ll play a central role in bringing these pieces together into cohesive, high-performance AI compute systems and driving the overall system from architecture through deployment
- Lead hardware system architecture from initial concept and requirements through design reviews, integration, and production deployment
- Translate workload and performance requirements into hardware decisions, working closely with cross-functional teams and external partners
- Lead architecture design across boards, chassis, and racks, including system topology, power, thermal, scale-up and scale-out fabrics, and networking
- Define and own bring-up, system integration, validation and qualification strategy
Our take
As the rate of AI adoption accelerates, Normal Computing is positioning itself to make advanced AI more usable and scalable for enterprises, where ris,, reliability and energy constraints remain major barriers. While generative AI has so far flourished in consumer settings, enterprise use has lagged due to concerns around control, auditability and cost. Normal's approach hopes to unlock these more complex use cases by rethinking how AI systems are built and deployed from the ground up.
At the core of the company's strategy is its Probabilistic AI framework, which gives customers greater control over reliability, adaptivity and auditability when working with private data. This software vision is now being reinforced by a major hardware milestone: the successful tape-out of CN101, the world's first thermodynamic computing chip. By harnessing natural physical dynamics such as stochasticity and noise, CN101 is designed to dramatically improve energy efficiency on AI and scientific workloads, allowing tasks like complex underwriting, probabilistic sampling and large-scale optimization to run faster and within fixed data centre energy budgets.
By tackling both the trust and energy bottlenecks that limit enterprise AI adoption, Normal is expanding its ambition from AI software into next-generation compute infrastructure. With a clear roadmap from CN101 to more advanced chips over the next few years, and substantial funding behind its R&D efforts, the company has the potential to play a meaningful role in shaping how high-performance AI scales in practice.
Freddie
Company Specialist at Welcome to the Jungle
Company
Funding (last 2 of 4 rounds)
Mar 2026
$50m
EARLY VC
Nov 2024
$67m
GRANT
Total funding: $134m
Company HQ
Civic Center, New York, NY
Leadership
Faris Sbahi
(Co-Founder & CEO)Previously worked as a Research Scientist at X, the moonshot factory, a Software Engineer at Google, and as a Forward Deployed Engineer at Palantir Technologies.
Matthias Tan
(Co-Founder & COO)Venture Partner at NextGen Venture Partners. Former Principal of Strategy and Partnerships at X, the moonshot factory, and Director of Product and Partnerships at Pony.ai.
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