Bioelectronics Engineer (Wearable Health Monitoring)

Company Description

InnerGize is a mental health wearable company focused on revolutionizing mental wellness by combatting stress. The company aims to help individuals find calm, feel better, and lead fuller lives with effective solutions. InnerGize believes in empowering people to take control of their well-being.

Role Description

We are looking for a Bioelectronics Engineer with strong expertise in signal processing, algorithm development, and health parameter monitoring for IoT-based wearable devices. The role will focus on working with multimodal biosignals (PPG, GSR/EDA, Accelerometer, ECG, EEG), involving signal acquisition, preprocessing, and time-series analysis. You will work closely with hardware, data science, and product teams to translate raw biosignal data into actionable health insights, focusing on stress, sleep, cognitive performance, and overall well-being. The ideal candidate will have hands-on experience in experimental research and machine learning for biological signal processing.

Requirements

Academic:

Master’s/PhD in Biomedical Engineering, Electrical/Electronics Engineering, Neuroengineering, or a related field.

Job-Specific:

Signal Processing Expertise: In-depth experience in signal preprocessing (filtering, noise reduction, artifact removal) for physiological signals such as PPG, ECG, EEG, GSR/EDA, and motion sensors.

Time Series Analysis: Proficiency in handling time-series data, feature extraction, and analysis of dynamic biosignals.

Hands-on Experimental Research: Direct experience with experimental research, including data collection, signal acquisition, and working with experimental hardware (sensors, wearable prototypes, etc.).

Python Expertise: Strong proficiency in Python for data processing, algorithm development, and machine learning model implementation (libraries like NumPy, SciPy, pandas, scikit-learn, TensorFlow, PyTorch).

Health Monitoring and DSP: Experience in Digital Signal Processing (DSP) and health parameter monitoring algorithms, specifically in applications related to biosignal analysis (heart rate variability, stress, sleep, cognitive performance).

Project Deployment & Machine Learning: At least 3 years of experience contributing to health monitoring systems, including real-world deployment or regulatory trials, with a strong understanding of machine learning models and mathematical modeling for biosignal analysis.