ML Engineer - Radiology AI & Production (Remote 2 days/wk)

Azmed; Paris; Hybride; EUR 70 000 - 110 000; Plein temps

Avantages offerts par ce poste

Remote work two days a week

Annual off-site company holiday

Young and dynamic team

Great office in Paris

Résumé du poste

AZmed is seeking a Machine Learning Engineer to join the AI team, bridging research and production systems. You will help automate radiology anomaly detection, enabling doctors to focus on life‑threatening exams, and collaborate with Ops, MLOps, and Software Engineering to deploy robust pipelines.

As part of a fast-growing team working with over 1000 sites in 40 countries, you will contribute to Rayvolve, AZmed’s AI-powered software, and help scale state‑of‑the‑art algorithms in radiology.

Qualifications

• Master’s degree in statistics, data science, AI or related field.
• 5+ years of experience in data engineering or data science with ML.
• Experience in Computer Vision and NLP for ML applications.
• Experience with medical images and model deployment is a plus.

Responsabilités

• Maintain quality and evolution of datasets.
• Maintain quality and evolution of models.
• Reduce time to production for AI models and operating points.
• Support AI-related feature developments.
• Improve and maintain the AI team’s codebase.
• Learn and spread development best practices in the team.
• Collaborate with Ops, MLOps, and Dev teams.

Connaissances

Computer Vision

NLP

Pytorch

Scikit-learn

DVC

Formation

Master’s degree in statistics, data science, AI

Outils

MongoDB

SQL

Spark

Airflow

DVC

Pytorch

skimage

Description du poste

AZmed is seeking a Machine Learning Engineer to join the AI team, bridging research and production systems. You will help automate radiology anomaly detection, enabling doctors to focus on life‑threatening exams, and collaborate with Ops, MLOps, and Software Engineering to deploy robust pipelines.

As part of a fast-growing team working with over 1000 sites in 40 countries, you will contribute to Rayvolve, AZmed’s AI-powered software, and help scale state‑of‑the‑art algorithms in radiology.