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 staff-level modeling architect to build the path from a production model or application to two things: a performance and energy number Neurophos will stand behind, and a functional model that software can boot against before tape-out.
What they're looking for
- BS, MS, or PhD in Computer Engineering, Electrical Engineering, Computer Science, or equivalent practical experience
- 8+ years of experience in hardware modeling, functional modeling, performance modeling, performance simulation, or accelerator performance analysis used by architects, RTL, compiler and runtime, or silicon teams. Graduate research may count toward this
- Track record of shipping a model or study that another team depended on, whether architecture, compiler, customer, or silicon
- Judgment to pick the right method for a given question among roofline, limiter analysis, analytical performance models, trace-driven simulation, transaction-level modeling (TLM), and RTL simulation
- Strong grounding in computer architecture, microarchitecture, memory systems, and AI accelerators, whether GPU, TPU, NPU, or custom SoC
- Modern C++ (C++17 or later) for functional models, performance models, and simulation infrastructure
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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