Neurophos develops photonic AI processing technology that focuses on hardware solutions for accelerating artificial intelligence inference by replacing traditional electronic compute elements with Optical Processing Units (OPUs). Backed by Purple Arch Ventures.
About the role
We are seeking a highly skilled Systems Engineer to own the architecture and requirements focus area for our optical vector-matrix multiplication (OVMM) engine product roadmap. This role is responsible for defining the OVMM signal-path architecture, decomposing product-level requirements into subsystem and interface specifications, and keeping that requirements set coherent, leveled, and traceable as the product evolves.
What they're looking for
- Degree: MS or PhD in Electrical Engineering, Applied Physics, or a closely related field, with emphasis on systems engineering or mixed-signal/optical system architecture
- Experience: 5+ years of professional experience in systems engineering, requirements management, or architecture definition for complex electro-optic, mixed-signal, or communications hardware
- Requirements Engineering: Demonstrated ability to build and maintain leveled requirements sets, interface control documents, and verification/traceability matrices, and to flow top-level requirements down into defensible subsystem allocations
- Architecture & Trade Studies: Proven ability to quantify architectural trade-offs (performance, power, complexity, cost) and to make and defend architecture decisions across disciplines
- System Modeling Literacy: Working fluency with signal-path and link-budget models (Python or MATLAB) sufficient to evaluate the impact of a trade study, even where day-to-day modeling is owned by others
- Communication: Excellent written and verbal communication, with the credibility to negotiate and align priorities across Responsible Engineers, functional leads, and executive stakeholders
More about this role
The demand for new data centers and AI compute is rapidly outpacing the planet's energy capacity. Digital solutions are hitting a power wall as we approach the physical limits of traditional silicon. Conquering this bottleneck means rethinking the fundamental architecture of inference compute. The industry's current path can't meet the need, so we're taking a different approach.
Instead of traditional electronic circuits, we use silicon photonics and an active, programmable metasurface to perform matrix multiplications at the speed of light. Our optical cells are 10,000x smaller than traditional photonic components, enabling unprecedented density. By using photonics instead of electricity, our chips become more efficient as they scale. This architecture will deliver up to 100 times the energy efficiency of existing solutions while significantly improving performance for large-scale AI inference.
We’ve assembled a world-class team of industry veterans and recently raised a $110M Series A led by Gates Frontier. Participants include M12 (Microsoft’s Venture Fund), Carbon Direct Capital, Aramco Ventures, Bosch Ventures, Tectonic Ventures, Space Capital, and others.
Join us and shape...
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