AI/ML Engineer
Machine Learning Engineer - Medical Technology
About the Role
We are supporting a growing Swiss medical technology company looking to strengthen its machine learning capabilities.
You will work on the development and implementation of machine-learning solutions for medical applications, taking projects from experimentation through validation and deployment.
Responsibilities
- Develop and improve machine-learning and deep-learning models for medical applications.
- Work with medical imaging and other complex healthcare datasets.
- Build reliable pipelines for training, testing and evaluating models.
- Improve model performance and prepare solutions for use within production environments.
- Support deployment of machine-learning models onto hardware-constrained systems.
- Work with software engineers to integrate ML solutions into wider product platforms.
- Contribute to data preparation, quality and annotation processes.
- Support testing, validation and technical documentation within a regulated development environment.
- Apply good software engineering and MLOps practices throughout development.
- Collaborate with technical and non-technical teams across the product lifecycle.
Requirements
- MSc or PhD in Machine Learning, AI, Computer Science, Biomedical Engineering or a related discipline.
- 2+ years of commercial ML/AI development experience.
- Strong Python and experience with frameworks such as PyTorch or TensorFlow.
- Experience with computer vision or medical imaging.
- Exposure to deploying and optimising ML models beyond the research environment.
- Understanding of edge, embedded or resource-constrained ML deployment.
- C/C++ knowledge would be beneficial.
- Previous experience within medical devices, healthcare technology or another regulated environment.
- Familiarity with MLOps, source control and modern software-development practices.
- Ultrasound experience would be highly advantageous but is not essential.
- Fluent English.
Only those with the right to work in Switzerland can be considered.
Only those shortlisted will be contacted.
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