Machine Learning Engineer – Optical Blood Pressure Estimation

Your Role

• Optical Signal Modelling & ML Development : Design, develop, and refine machine-learning models that estimate blood pressure from optical data (PPG / rPPG, RGB video) captured via smartphone cameras
• Signal Processing & Feature Extraction : Work hands-on with camera-derived biomedical signals, applying optical and physiological signal-processing methods to build robust input pipelines
• Model Improvement & Iterative Experimentation : Analyze model behaviour, run systematic experiments, and drive continuous iteration to enhance accuracy, robustness, and generalization across diverse conditions
• Production Readiness & Deployment Support : Prepare ML models for deployment on mobile devices and cloud systems, collaborating with engineering to ensure smooth integration and medical-grade reliability
• End-to-End Ownership of the Modelling Pipeline : Take full responsibility for the ML workflow—from data preparation to modelling, validation, documentation, refinement, and performance tracking
• Cross-Functional Collaboration : Work closely with colleagues in AI / Data Science, engineering, physiology, and software to ensure solutions are technically sound and aligned with product needs
• Leading Role in Optical BP Estimation : Advance a novel ML technology application : shape, optimize, and mature an emerging ML capability central to the Hilo Lens product line

Your Profile

MSc or PhD in Computer Science, Machine Learning, Mathematics, Electrical Engineering, Biomedical Engineering, or a related technical field

Minimum 2+ years of applied industry experience in machine learning or deep learning, ideally in MedTech or regulated domains. You have hands-on experience developing, fine-tuning, iterating, and validating neural network models on real-world datasets, including conducting systematic experiments and driving performance improvements through continuous evaluation

You have strong programming skills in Python and hands-on experience with modern ML frameworks. You are skilled in building, validating, and deploying machine-learning models and bring a solid foundation in statistical learning and time-series modelling, including CNNs and RNNs. In addition, you bring applied signal-processing experience, particularly with PPG, ECG, or motion-supported signals.

Ideally, you bring experience from start-ups or innovation-driven environments with broad, end-to-end ownership, and exposure to regulated or technically demanding industries, computer vision, or sensor technology

English at highly proficient level is a must. French, German or Italian are advantageous.

A creative, proactive problem-solver, you enjoy turning complex challenges into real, working solutions. You love getting hands-on — building, testing, and iterating end-to-end — and you naturally take ownership of your work. You stay organized, move things forward independently, and collaborate openly with the people around you.