On-device ML and app development for patient-monitoring rings
On-device machine learning and cross-platform application work for rings that monitor patients in long-term care.
- 2025—
- Full-time role
- Full-stack developer
The situation
A ring worn continuously has a power budget measured in days, and a wearer in long-term care who will not be charging it on a schedule. That constraint decides the architecture before any product question does: inference has to happen on the device, because streaming raw signal to the cloud would exhaust the battery long before it produced anything useful.
What the work involved
- Engineered power-efficient machine learning models for on-device analytics, where the power budget rules out cloud inference.
- Developed front and back end in React Native, so a single codebase serves every platform the product ships on.
Stack
On-device ML · IoT · React Native · Real-time systems
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