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AI/ML Engineer

Pubblicato il 05-10-2026 - MetaSensing in Milano

- Job purpose:to build computer vision pipelines for Synthetic Aperture Radar (SAR) object detection and classification. You will apply modern deep learning techniques to airborne, spaceborne, and simulated radar data to extract actionable intelligence.
- Employment condition:Full-time, 40 hours per week.

Si candidi rapidamente per massimizzare le sue possibilità di essere considerato/a per un colloquio. Legga la descrizione completa del lavoro qui sotto.
Candidate must be eligible to work in the EU.

Company description:
MetaSensingis an innovative Italian remote sensing company founded in 2008 with the aim of commercializing the new compact, high resolution radar mapping technology.

In just a few years,MetaSensinghasacquirednumeroussuccessful airborne radar campaigns completed at the Ka, Ku, X, C, S, L and P frequency bands, and developed drone and space-based radar systems for different applications.





TodayMetaSensingis one of the unique companies in the world which offer cost effective airborne and space-based radar sensors and services both for commercial and security applications.

Combination of innovative technology and advanced processing techniques has madeMetaSensingafast growing, global company with projects in Europe,South EastAsia, Middle East, North and South America.

Job Duties:

-
- Develop and optimize deep learning models for object detection and classification in SAR imagery
- Train and evaluate models on airborne, spaceborne, and simulated radar data
- Adapt models trained on simulated SAR data to real-world radar signatures
- Collaborate with radar engineers to account for SAR-specific characteristics such as speckle, shadowing, and acquisition geometry
- Work with the Radar Engineer to define dataset requirements
- Conduct error analysis and iterate on model architecture, training strategy, and data selection

Required skills and qualifications:

-
- Strong proficiency in Python
- Hands-on experience training deep learning models with PyTor

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