Machine Learning Engineer – Optical Blood Pressure Estimation

• 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

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

• Professional Expertise 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

• Technical Experience 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.

• Industry Fit 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

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

• Personality 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.

We're looking for a hands-on builder who has developed, iterated, and optimized machine-learning models for real-world signals - someone who thrives where algorithms meet real users and who's excited to drive camera-based blood-pressure estimation in digital health.

Imagine leveraging your expertise in machine learning, signal modeling, and data-driven health technology to directly improve the lives of millions of people living with hypertension. Hilo by Aktiia is revolutionizing how cardiovascular health is monitored and managed. They've developed and commercialized the world's first continuous blood pressure monitor, empowering both patients and physicians with unprecedented insights into blood-pressure patterns. Backed by over $100M in funding from top-tier investors across Europe and the US, Hilo is a fast-growing startup rooted in over 20 years of research at the prestigious Swiss Center for Electronics and Microtechnology (CSEM). Their CE-certified, Class IIa medical device is already available in over 10 countries worldwide and they are continuing to expand their proven technology and market presence. As Aktiia continues to advance its technology, we're looking for a highly hands-on Machine Learning Engineer who can take end-to-end ownership of the optical blood-pressure estimation pipeline - transforming camera-derived optical signals into accurate, production-ready blood-pressure models and contributing to a core capability within the Hilo Lens product line.