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Data Engineer (Detect & Track Distillation)
Harmattan AI
Paris, USA
Full-time
Mid
Onsite
Discovered 1 weeks ago
pythonpytorch
Free
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Role Overview
Our ML teams train models on datasets derived from large volumes of raw, unstructured data. Model quality depends directly on data quality, and today that data is handled largely by hand.
As a Data Engineer, operating out of Paris, Lausanne, or Zurich, you will own the data layer that feeds the team's models, from raw field logs or public datasets through curated, versioned, training-ready datasets. You will manage terabytes of raw, unstructured data and turn it into clean, documented, versioned datasets, so that the modelers spend their time designing and training models, not waiting on data loaders or wrangling corrupted files. You join at an early stage with real influence over how field and public data gets processed for deep-learning pipelines.
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About Us
Harmattan AI is a next-generation defense prime building autonomous and scalable defense systems. Following the close of a $200M Series B, valuing the company at $1.4 billion, we are expanding our teams and capabilities to deliver mission-critical systems to allied forces.
Our work is guided by clear values: building technologies with real-world impact, pursuing excellence in everything we do, setting ambitious goals, and taking on the hardest technical challenges. We operate in a demanding environment where rigor, ownership, and execution are expected.
ABOUT THE ROLE
Our ML teams train models on datasets derived from large volumes of raw, unstructured data. Model quality depends directly on data quality, and today that data is handled largely by hand.
As a Data Engineer, operating out of Paris, Lausanne, or Zurich, you will own the data layer that feeds the team's models, from raw field logs or public datasets through curated, versioned, training-ready datasets. You will manage terabytes of raw, unstructured data and turn it into clean, documented, versioned datasets, so that the modelers spend their time designing and training models, not waiting on data loaders or wrangling corrupted files. You join at an early stage with real influence over how field and public data gets processed for deep-learning pipelines.
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