Real-Time Computer Vision Engineer, Satellite Imagery

Comand AI; Paris; On-site; EUR 90,000 - 130,000; Full time

Job summary

Comand AI is seeking a Computer Vision Engineer to tackle hard perception problems in satellite and aerial imagery. You will design and train architectures for detection, segmentation, and classification across complex image types, and ship models into production on short cycles.

You will push the state of the art, iterate fast, and collaborate with data and software engineers to ensure robust deployment, while spending time with end users to define what success looks like in practice.

Qualifications

• 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; computer vision experience strongly preferred.
• Solid Python, Git, and PyTorch. JAX/Flax, scikit-learn, and Numpy are real pluses.

Responsibilities

• 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.

Skills

Strong deep learning

Computer vision

Python

PyTorch

Git

Transformer

Multimodal architectures

English fluency

Education

PhD in AI / deep learning / computer vision

Tools

JAX/Flax

scikit-learn

Numpy

Job description

Comand AI is seeking a Computer Vision Engineer to tackle hard perception problems in satellite and aerial imagery. You will design and train architectures for detection, segmentation, and classification across complex image types, and ship models into production on short cycles.

You will push the state of the art, iterate fast, and collaborate with data and software engineers to ensure robust deployment, while spending time with end users to define what success looks like in practice.