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 calibration architecture, system integration, and verification for our optical vector-matrix multiplication (OVMM) engine. This role is central to closing the gap between a system that is designed to meet its accuracy targets and one that demonstrably does so on the bench: you will devise the calibration algorithms that correct optical phase, amplitude, and timing across many channels, specify the analog and digital hardware structures needed for real-time...
What they're looking for
- Degree: MS or PhD in Electrical Engineering, Applied Physics, or a closely related field, with emphasis on control systems, signal processing, or mixed-signal systems
- Experience: 5+ years of professional experience in calibration algorithm development, system bring-up, or verification of complex electro-optic or mixed-signal systems
- Lab & Verification: Hands-on experience bringing up and debugging complex hardware using oscilloscopes, spectrum/network analyzers, and optical test equipment, and correlating measured performance against a model
- Software for Calibration: Proficiency developing calibration and test software (Python and/or MATLAB), including control loops, data acquisition, and automated characterization scripts, sufficient to run and iterate on lab measurements independently
- Communication: Excellent communication skills for cross-functional collaboration with analog IC designers, digital designers, photonic device engineers, and system architects
- Practical experience with digital signal processing techniques, including spectral analysis, correlation, and adaptive filtering, applied within calibration or measurement loops
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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