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Senior Signal Processing Engineer

CompraTica Empleos

EMP:Technology
Paris Offices
Tiempo Completo
Remoto
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Descripción

Meet Arago and the AragoniansArago is an AI and computer hardware company whose mission is to drive the course of history forward.

We do so by accelerating breakthroughs at the intersection of AI and semiconductors.

Founded in 2024 by AI researchers and physicists with deep expertise in photonics, electronics, software, mathematics, and machine learning, Arago brings together a lean team of engineers and scientists from the world’s top companies and research labs.

Composed of nine nationalities and operating from hubs in France, North America, and Israel, we believe in great science and fast achievements.

Our work is guided by these core principles:Do great things: we deliver work we’re proud to sign our name to.

High velocity: speed matters.

We move quickly, one step at a time.

One unit: we’re all in this together, with relationships grounded in trust, respect, and camaraderie.

Arago is backed by executives from Apple, Arm, Nvidia, Microsoft, and Hugging Face, as well as prominent US and European deeptech venture firms and exited founders.

What you’ll doAs a Senior Signal Processing Engineer, you sit at the intersection of our physical and digital layers, translating hardware reality into mathematical clarity.

You will work hands-on with the optics, analog, and digital teams to co-optimize the design of our compute engine.

You're a strong fit if C/C++ is your primary tool and your background spans environments such as telecom, high-speed sensing, optimal control, signal processing, or related fields.

Habilidades

and ExperienceExperience: Minimum 5 years of experience in relevant industries (telecom, high-speed sensors, optimal control, signal processing, or related fields).Programming: Extensive experience in C/C++ and Python.Education: Major in Mathematics, Physics, or Informatics with deep expertise in numerical analysis, optimization, and mathematical modeling.Systems Modeling: Strong ability to identify and model dominant sources of variability, noise, and dist...

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