As a Medical Research Engineer, you join the Clinical Data Science team within Digital Health Solutions, Hamilton Medical's Software as a Medical Device (SaMD) business. Digital Health Solutions develops cloud-based applications including a Clinical Decision Support System (CDSS) for critical care ventilation; our team develops data-driven features and applications for it. You take ownership of your topics in a startup-like setting and help us grow these capabilities further as the products scale. 

Building on a solid understanding of ventilation principles, you design, develop, and validate signal processing and machine learning algorithms that address a wide range of challenges across mechanical ventilation. You own problems from research question through to product-ready solutions, working hands-on with clinical experts and engineers to move prototypes into clinical validation and, ultimately, into products that reach the bedside. 

Your key responsibilities 

• Design, implement, and rigorously evaluate signal processing, data science, and machine learning algorithms across mechanical ventilation 

• Take ownership of research topics end-to-end, from problem framing and data exploration to validated, product-oriented solutions 

• Build and run data analyses and complete ML workflows using our Azure-based cloud data platform 

• Develop reproducible, well-engineered pipelines that support fast iteration while meeting the traceability and documentation needs of a regulated environment 

• Work closely with clinical experts, software engineers, and product stakeholders across disciplines 

Must-have 

• A PhD in a relevant engineering or scientific field (biomedical engineering, computer science, physics, or related), or equivalent industry experience 

• Several years of experience developing and validating data science / machine learning solutions 

• Strong programming skills in Python; solid grounding in data and signal processing 

• Experience with cloud-based data and ML tooling 

• Analytical, independent, and meticulous work style 

• Openness to interdisciplinary collaboration and a product-oriented mindset 

Beneficial 

• Prior experience developing medical-device software, ideally through to production 

• Working knowledge of Microsoft Azure (Azure Databricks, Azure Machine Learning) 

• Familiarity with MLOps practices (experiment tracking, model deployment, monitoring)