About Neurophos

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 the future of computing!

Location: Austin, TX. Full-time onsite position.

Reports To: Chief Systems Engineer

FLSA Status: Exempt

Position Overview

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 calibration execution, define the bring-up and verification strategy, and personally drive laboratory characterization of the hardware.

You will spend much of your time devising calibration algorithms grounded in control theory and signal processing, and the rest of it hands-on in the lab, bringing up hardware, running verification plans, characterizing performance, and tracking down exactly where measured behavior departs from the model. If you combine control-theory and signal-processing depth with genuine enthusiasm for lab work, this role will let you own the path from calibrated silicon to a working, verified system.

Key Responsibilities

Primary Responsibilities

Calibration Architecture: Define the calibration architecture for the OVMM system, and scope and size the algorithms needed to correct optical phase, amplitude, and timing across many transmit and receive channels.

Calibration Algorithm Development: Design, implement, and optimize calibration algorithms, applying control theory and optimization techniques to minimize calibration time while meeting system accuracy targets.

Signal Processing for Calibration: Design and implement signal processing techniques used within calibration loops to enhance measurement SNR through coding gain and noise bandwidth reduction.

Calibration Data Compression: Develop techniques and algorithms to compress calibration data and minimize on-chip and off-chip storage requirements.

HW/SW/Control Interaction: Specify the calibration controller behavior, apply paths, and digital hardware structures needed to execute calibration in silicon, and define the calibration software and firmware interfaces that drive them.

System Integration Strategy & Bring-Up: Define the system integration strategy, plan the integration sequence, and lead laboratory bring-up and debug of the assembled system, establishing acceptance criteria at each subassembly boundary.

Observability & Diagnostics: Define system-level test, diagnostic, and observability requirements so that bring-up, calibration, and field operation can all distinguish a healthy system from a degraded one.

Verification & Characterization: Develop verification plans, execute performance characterization in the lab, and correlate measured results with modeled predictions, identifying and root-causing gaps between the two.