Principal Biomedical Engineer

We anticipate the application window for this opening will close on - 13 Jul 2026.

At Mini Med, you can begin a lifelong career of exploration and innovation, while helping make a difference in the lives of people living with diabetes around the globe. You'll lead with purpose, breaking down barriers to innovation for a more connected, compassionate world.

As a member of the Minimed’s – Continuous Glucose Monitoring Sensor R&D team the Principal Biomedical Engineer will be responsible for providing technical and scientific leadership for the design and development of next‑generation continuous glucose monitoring (CGM) sensors.

This individual in this role will have deep understanding of how wearable electrochemical sensors function, including how the electrode geometry, enzyme loading, membrane stacks and tissue interface govern sensor performance, manufacturability, and reliability. This foundational sensor knowledge is critical in ensuring that statistical model development and analytical frameworks reflect true sensor physics.

The individual in this role will own and lead key elements of design transfer into manufacturing, including applying statistically robust methods for defining and refining in‑process check specifications, statistically justified sample size determination, and substrate rejection criteria. As part of continuous improvement activities, the individual in this role will apply machine learning tools to improve upon current process monitoring technologies. The work will focus on technologies and methods that enable improved sensor design and manufacturability.

This Principal Biomedical Engineer will partner cross functionally to highlight insights from real‑world sensor performance data, driving continuous sensor improvement initiatives and directly informing next‑generation sensing platform designs. This role is intended for a recognized technical leader who can mentor strong teams, influence senior decisions, and help strengthen Mini Med's culture of innovation, analytical rigor, and patient impact.

• Leads key elements of the design transfer activities, including defining and refining statistically robust in‑process check specifications, statistically justified sample size determination, and rejection criteria.
• Leads advanced statistical analysis and data‑driven investigations to support sensor process optimization, performance modeling, and manufacturing decision‑making.
• Applies first‑principles understanding of glucose sensor behavior, including electrochemistry, enzyme‑mediated sensing, mass transport, membrane design, tissue interface, interferents, drift, and stability.
• Applies data analysis and statistical methodologies to generate insights on sensor performance, drive experiment design and project next steps.
• Applies analytical tools, including statistical learning methods where appropriate, to support defect detection, classification, and sensor process monitoring improvements.
• Utilizes technical skills associated with Six Sigma, Lean, DRM/DFSS and other appropriate continuous improvement techniques.
• Mentors scientists and engineers to promote rigorous experimental design, and strengthens first‑principles technical decision making across Sensor R&D.
• Ensures work is performed in compliance with applicable quality system requirements, design controls, medical device standards, and regulatory expectations.

Bachelor’s degree with 7+ years of relevant experience; or advanced degree with 5+ years of relevant experience.

• Experience in glucose sensor, biosensor, electrochemical sensor, wearable sensor, or related medical device sensor development with working knowledge of how sensor design parameters (e.g., electrode geometry, enzyme loading, membrane stacks) affect sensor performance, reliability, and manufacturing feasibility.
• Demonstrated hands‑on experience in sensor design, first‑principles problem solving, structured experimentation, and data‑driven decision making.
• Demonstrated expertise in advanced statistical methods: DOE, ANOVA, mixed‑effects models, hypothesis testing, SPC, process…