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TinyML Engineer | Deep-Tech R&D | Germany (On-Site)

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Stellenbeschreibung:

TinyML Engineer | Deep-Tech R&D Tech Unicorn | Germany (On-Site)

A very exciting & unusual deep tech start-up, especially in the context of advanced robotics and AI are looking for an exceptional Tiny Machine Learning Engineer to join their headquarters based near Cologne.

  • A rare chance to join a rapidly scaling company operating at the frontier of embedded AI
  • Work on ultra-low-power ML systems in an environment defined by innovation, experimentation, and technical ambition
  • Be part of a multidisciplinary team combining embedded engineering, and advanced sensing
  • Shape the next generation of intelligent edge devices designed for real-time environmental awareness and control

Role:

  • Design, deploy & optimize TinyML models on microcontrollers (ARM Cortex-M, RISC-V, custom accelerators) used on living insect biobot systems
  • Developing real-time sensing and control pipelines
  • Applying advanced optimisation techniques (quantisation, pruning, compression)
  • Integrating ML models with advanced embedded systems
  • Shaping the company’s next generation of intelligent edge platforms

Skills:

  • Embedded ML / TinyML experience
  • Strong background in C/C++, Python, & microcontroller platforms
  • Experience with CMSIS-NN, Tensorflow lite, Edge Impulse
  • Background with microcontrollers such as STM32, ESP32, or Nordic platforms
  • Strong understanding of machine learning and deep learning architectures

This is a very unique opportunity as you will have the chance to build an entirely new category of robotics.

We are particularly interested in engineers who combine low-level embedded expertise with strong ML intuition, and who thrive in highly experimental deep-tech environments.

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NOTE / HINWEIS:
EnglishEN: Please refer to Fuchsjobs for the source of your application
DeutschDE: Bitte erwähne Fuchsjobs, als Quelle Deiner Bewerbung

Stelleninformationen

  • Veröffentlichungsdatum:

    11 Mär 2026
  • Standort:

  • Typ:

    Vollzeit
  • Arbeitsmodell:

    Vor Ort
  • Kategorie:

  • Erfahrung:

    2+ years
  • Arbeitsverhältnis:

    Angestellt

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