ML Engineer – Physiological Signal Analysis

Position Overview

We are seeking a full‑time Machine Learning Engineer with strong expertise in signal processing and predictive modeling for physiological sensor data. The role focuses on developing and validating models using photoplethysmography (PPG) and related multimodal signals to derive meaningful health insights. The ideal candidate will be passionate about advancing healthcare technology and eager to build reliable, clinically relevant AI models that bridge wearable sensing and digital health applications.

Key Responsibilities

• Develop, optimize, and evaluate ML models for physiological sensor data, focusing on extracting clinically meaningful insights from multimodal signals such as PPG, accelerometer, and ECG.
• Design preprocessing and feature-extraction pipelines for time-series data and implement signal-quality assessment techniques.
• Set up and operate hardware platforms for data collection; collaborate with clinicians and research partners to design and manage studies, ensuring high-quality data collection.
• Implement signal-quality assessment and noise-reduction techniques to enhance model robustness.
• Collaborate with data scientists and clinicians to interpret model outputs in a clinical context.
• Integrate ML pipelines into production systems (cloud or edge) and support model deployment.
• Conduct literature reviews in biomedical signal processing and machine learning for health applications.
• Contribute to research documentation, publications, and regulatory validation processes.

Qualifications

About Us :

CIPRA.ai is an emerging startup in the digital health sector, specializing in innovative solutions for chronic disease management, including hypertension and diabetes. Our mission is to provide personalized and proactive healthcare that integrates key lifestyle factors for managing chronic conditions. We develop cloud-based AI platforms and wearable technologies designed for efficient patient engagement and superior healthcare outcomes.

Our technology has been validated through multiple clinical trials at leading institutions over the past six years, and we have established partnerships and commercial rollouts with health systems in the United States and around the world.