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# Computer Vision Engineer

- **Company:** Comand AI
- **Location:** Paris, France
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Geographic Information Systems, Artificial Intelligence, Computer Vision, Github, Python (Programming Language), PostgreSQL, NumPy, Object Detection, Open Source Intelligence, SQL Databases, Integrated Service Routers, Pytorch, Deep Learning, Git, Scikit Learn
- **Published:** July 8, 2026
- **Apply:** https://fr.indeed.com/viewjob?jk=f46908ace9ec2f79

## About the Role

We want scientific depth combined with the instinct to ship. Not a publication count, but evidence of both: a GitHub and Google Scholar that tell a coherent story. A recent PhD is one path in. A more experienced researcher with a strong collaborative instinct and a track record of shipping is equally welcome., * PhD in AI, deep learning, or computer vision, or 5-10 years of serious applied research with a track record to back it up

 * Strong deep learning expertise, non-negotiable. Computer vision experience strongly preferred
 * Solid Python, Git, and PyTorch. JAX/Flax, scikit-learn, and Numpy are real pluses
 * Hands-on experience with Transformer and multimodal architectures applied to visual data
 * EU citizenship (required by our defense contracts)
 * English fluency
 * A genuine appetite for cross-functional work: you explain your technical choices, co-design with product and operational teams, and take ownership of outcomes rather than just models

Nice to have:

 * Applied work with satellite, aerial, or drone imagery
 * Experience deploying models to constrained or sovereign environments
 * SQL/PostgreSQL
 * Prior work in defense, dual-use, or operational tech
 * Geospatial data processing background

## Description

As a Computer Vision Engineer, you own the hardest perception problems in the stack. The job is to crack the scientific problems that prevent us from turning multi-source sensor data (satellite, drone, ISR, OSINT) into real-time operational clarity. Your models ship into production on short cycles, used by people for whom bad output has real consequences.

In practice:

 * Design and train architectures for object detection and segmentation on satellite and aerial imagery, and classification across complex image types
 * Drive our applied research agenda: track the state of the art rigorously, form hypotheses, iterate fast
 * Push models to production readiness: optimize for inference speed and robustness under distribution shift, which will happen in field conditions
 * Work closely with data engineers and software engineers to move work from research to production without losing what matters
 * Spend time with end users and domain experts to understand what "good" actually means in their context


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