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 modeling architect for hands-on architecture modeling of the T100 optical inference accelerator, with hardware/software co-design in the loop. You will work alongside senior engineers across two tracks. The first is analytical and system performance: roofline and limiter analyses, architecture performance models, workload setup, and the resulting plots and reports.
What they're looking for
- BS or MS in Computer Engineering, Electrical Engineering, Computer Science, or a related field
- 3+ years of experience in hardware modeling, performance simulation, computer architecture, or related work
- Proficiency in Python or modern C++ (C++17 or later). Python-first and C++-first backgrounds are both welcome
- Working knowledge of computer architecture and microarchitecture, including pipelines, caches, memory hierarchies, and instruction set architecture (ISA)
- Ability to turn an LLM, GEMM, or accelerator paper into a workload config using Hugging Face or PyTorch
- Strong debugging skills and the habit of writing down what was run, what was assumed, and what the number means
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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