Algorithm Engineer

Join our team as an Algorithm Engineer, where you will lead the entire biosignal-based algorithm development lifecycle for medical devices. You will be responsible for specifications and requirements gathering, data curation and labeling, development, failure-analysis, production, maintenance, and documentation. You will also enhance our internal deep learning and machine learning tools, spread and improve best practices, present results to key stakeholders, and support client-facing projects. The ideal candidate will have over 4 years of industry experience in machine learning and deep learning, particularly in health sciences or other regulated fields.

• Participer à l'ensemble du cycle de développement des algorithmes basés sur les biosignaux pour les dispositifs médicaux, y compris la collecte des spécifications et des exigences, la curation et l'étiquetage des données, le développement, l'analyse des échecs, la production, la maintenance et la documentation.
• Sélectionner, mettre en œuvre et développer la méthode la plus appropriée pour chaque problème, en sachant quand appliquer des techniques d'apprentissage profond et quand d'autres méthodes sont plus efficaces.
• Améliorer nos outils internes d'apprentissage profond et d'apprentissage automatique pour augmenter l'efficacité de l'équipe, introduire de nouvelles architectures de modèles et des techniques algorithmiques, et affiner la base de code pour encourager la réutilisabilité.
• You are familiar with biosignals, medical imaging data, or large time-series datasets, or are enthusiastic about learning more in the domain
• You are excited to participate in the entire algorithm development lifecycle, which spans scoping, data wrangling, algorithm development/experimentation, formal validation, quality/regulatory documentation, production deployment, and working with clients who might benefit from these algorithms
• You are proficient in using PyTorch (preferred) or other deep learning frameworks for training, developing, and deploying deep learning models
• You are familiar with latest Deep Learning advances (Transformer/ViT, large scale modeling, large model training, ...)
• You are experienced with digital signal processing (DSP) and statistics and care about using the right tool for the job, which in many cases might not be machine learning or deep learning
• You are able to distill, discuss, and present complex technical topics in a way that is appropriate for the audience at hand, both internally and externally
• You thrive in a team environment, recognizing that collaboration, open communication, and continuous feedback are essential for collective success
• You have more than 4 years of industry experience in machine learning and deep learning, particularly in health sciences or other regulated fields, with a proven track record of bringing algorithms into production
• You follow and adopt best practices in software and ML engineering, including testing, version control, code reviews, documentation, Dockerization, CI/CD, and experiment tracking