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 quantitative, end-to-end performance model of our optical vector-matrix multiplication (OVMM) engine. This role is the technical center of Systems Engineering's performance-modeling focus area: you will lead the end-to-end modeling, analysis, and architectural definition of high-speed communication and signal-processing systems.
What they're looking for
- Degree: MS or PhD in Electrical Engineering, Applied Physics, or a closely related field, with emphasis on communication theory, signal processing, or mixed-signal systems
- Experience: 5+ years of professional experience in communication systems architecture, link-budget/signal-chain modeling, or performance modeling of optical or high-speed electrical communication systems
- Communication Theory & Signal Processing: Deep knowledge of modulation formats, equalization, synchronization and estimation, quantization effects, and coding-gain concepts, and how each factors into an end-to-end performance budget
- System Modeling: Expert-level proficiency in Python (NumPy/SciPy) and/or MATLAB/Simulink for behavioral modeling, Monte Carlo simulation, and statistical/time-domain analysis
- Link Budget & Noise Analysis: Demonstrated ownership of an end-to-end optical or RF link budget (or equivalent SNR/noise budget), including the ability to update allocations when a component or architecture choice changes and predict the system-level impact
- Communication: Excellent written and verbal communication for cross-functional collaboration with analog, digital, photonic, and software engineers, and the ability to present quantitative trade-offs clearly to technical leadership
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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