namR is using state-of-the art machine learning and deep learning methods to build a geospatial database containing information about territories, buildings, cities and more.

Our computer vision team designs deep learning pipelines aiming to extract meaningful information from street level, aerial or satellite imagery.

Your mission will be to:

• Develop Computer Vision models for extracting information from aerial, satellite or urban-level imagery, or any type of rasterized data;

• Oversee CV projects from commercial specifications through to production, collaborating with other technical teams to develop and implement data products;

• Develop and maintain CV models production and related analysis tools, as well as image processing and visualization;

• Participate in the development of the Computer Vision team’s codebase;

• Proactively monitor new techniques, technologies and algorithms related to deep learning and Computer Vision.

Education:

Master 2 in mathematics, statistics, data science, computer science or engineering school.

Experience:

2+ years of experience in computer vision.

Technical skills required:

• Proficiency in computer vision techniques and models;

• Proficiency in Python and its standard libraries;

• Deep learning experience using advanced frameworks (PyTorch, Tensorflow, sklearn, …);

• Strong interest in modeling and understanding of the inner workings of computer vision models

• Good development skills and practices, solid experience with Git.

Additional technical skills:

• Ability to read, understand and report on the state of the art in computer vision;

• Basic SQL skills;

• Be comfortable with the Linux environment;

• A good command of software components to deploy a model in production (cloud environments) is a plus.

Soft skills:

• Curiosity;

• Team spirit;

• Rigor;

• Strong written and verbal communication skills in French (fluent) and English (working proficiency).

Would you like to join a dynamic and innovative team to participate in an ambitious impact project? Apply!

1. Interview with HR Lead & Computer Vision Lead

2. Technical test

3. Team meeting