Health Sensing ML Engineer
Core
Develop and implement machine learning and deep learning models for health sensing applications using wearable sensor data.
Role type
Machine Learning Engineer (Health Sensing)
Builds
ML algorithms for PPG, accelerometer, and ECG sensors
Domain
Health technology / Biomedical signal processing
Deliverable
production ML models
Required skills
Python, PyTorch, TensorFlow, time-series data analysis, sensor data processing, biomedical signal processing, data preprocessing, feature extraction, model evaluation, distributed training
Preferred skills
MS or PhD, clinical domain experience, collaborative research environment experience
Technologies
PyTorch, TensorFlow
Responsibilities
Develop and implement ML and deep learning models using health sensing data; Analyze large-scale health data from wearable sensors to extract insights; Work across the entire ML development cycle from data pipelines to model evaluation; Analyze model behavior and drive design decisions with failure analysis; Build end-to-end pipelines for rapid iterations; Work multi-functionally to bring algorithms to real-world applications with clinical and engineering partners