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Azmed

Azmed

ML Engineer (Senior)

Paris, FRSur siteCDITemps plein

Publié 23 sept. 2026

Description

We are an enthusiastic, passionate, and hardworking team looking for a like-minded ML engineer.

Doctors waste a lot of time looking for abnormalities in X-rays. At AZmed, you will help automate this detection process, so doctors can spend most of their time on life-threatening exams.

As a Machine Learning Engineer in the AI team, you will be a bridge between experimental research and robust production systems, directly empowering our AI researchers to build, scale, and deploy cutting-edge technologies. In this supportive yet highly impactful role, your core focus will be to:

  • Maintain the overall quality and continuous evolution of both our datasets and our ML models.

  • Take ownership of the AI team’s codebase, refining architecture, enforcing high standards through rigorous pull request reviews, and actively spreading modern software engineering best practices across the team.

  • Optimize and operate our Foundation Model pipeline (data preparation, training, validation).

  • Drive AI-related feature development and build streamlined pipelines to significantly reduce the time it takes to move models and operating points into production environments.

Success in this role relies on seamless cross-functional collaboration, as you will partner closely with our Ops, MLOps, and Software Engineering teams to ensure smooth infrastructure integration and system reliability.

Why join AZmed?

You will work within the whole R&D team (developers, data scientists, and MLOps engineers) and doctors on a daily basis. We are going through a fast growth period and are working with over 1000 sites in 40 countries, so it’s an exciting time to join our team and contribute to AZmed's mission.

AZmed’s first product is Rayvolve, an AI-based abnormality detection software, and you will have hands-on experience in developing applications that integrate state-of-the-art algorithms in the first French deep learning software in radiology.

Responsibilities

  • Ensure quality and continuous improvement of our datasets

  • Ensure quality and continuous improvement of our models

  • Reduce time to production of AI models and operating points

  • Support AI-related feature developments

  • Improve and maintain the AI team’s codebase

  • Learn and spread dev good practices and guidelines in the team

  • Collaborate closely with the Ops, MLOps and Dev teams

Qualifications

  • Master’s degree in statistics, data science, AI (or any related domain)

  • 5+ years of experience as a data engineer or data scientist working on ML applications

  • Experience in Computer Vision (Classification, Detection and Segmentation)

  • Experience in NLP (LLM, pre-processing, word embeddings)

  • Experience with our Data Engineering stack (Mongodb, SQL, Spark, Airflow, dvc)

  • Experience with our ML stack (pytorch, sklearn, skimage)

  • Have a strong sense of responsibility and rigor, from PR Reviews to ownership of features

  • Being proactive in proposing new features or technologies

Bonus points

  • Experience with medical images

  • Experience with image and text alignment, VLM, LLaVA

  • Experience in training or fine-tuning foundation models (pipeline and resource management)

Perks & Benefits

  • Flexible work : Possibility of working remotely two days per week 🌻

  • Annual off-site : One company holiday each year in amazing locations 🪅

  • Atmosphere : Young and dynamique team (29 y.o average) 🐣

  • Localisation : Great office in Paris 2nd arrondissement (Grands Boulevards) 🏣

  • Screening interview with our Head Of AI (30 min, remote)

  • Technical interview with our Deep Learning Researchers (2 hours, at the office or remote)

  • Meet the team and founders (2h30 hours, at the office)

Aperçu du poste

Type de poste

Temps plein

E-mail

liza@azmed.co

Compétences requises

Computer Vision (classification, detection, segmentation)Natural Language Processing (LLMs, preprocessing, word embeddings)PyTorchscikit-learnMongoDBSQLApache SparkApache AirflowDVC (data versioning)Dataset quality assurance and continuous dataset improvementFoundation model pipeline optimization (data preparation, training, validation)Model deployment and MLOps (reducing time-to-production, CI/CD for ML)Codebase ownership, PR reviews and software engineering best practicesCross-functional collaboration with Ops, MLOps and Software Engineering teamsMedical imaging domain knowledge (working with medical images)

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