Responsibilities
- Design and implement signal processing algorithms for extracting vital signs from video-based sensors (rPPG, rBP)
- Develop real-time filtering, denoising, and feature extraction methods to achieve clinical-grade accuracy
- Build validation pipelines comparing our video-based measurements against clinical reference devices (ECG, PPG, BP monitors)
- Help design studies that ensure millisecond-level synchronization between video streams and reference sensors for accurate ground truth
- Integrate with the computer vision pipeline (C++) to leverage multi-modal signal enhancement techniques
- Architect cloud-based QA and automated verification systems for reproducible algorithm validation
- Prototype algorithms in Python and C++ before deploying them on our production system
Requirements
- Master's degree in Electrical Engineering, Biomedical Engineering, or related field (or PhD)
- Strong fundamentals in digital signal processing, statistical methods, and real-time systems
- Deep expertise in physiological signal processing (PPG, ECG, respiration, blood pressure)
- Experience processing low-frequency physiological signals with challenging SNR characteristics
- Proficiency in both Python (prototyping) and C++ (production integration)
- Experience with spectral analysis, adaptive filtering, and time-domain methods for real-time applications
- Ability to work independently, solve complex problems, and drive projects to completion
- 5+ years industry experience developing signal processing algorithms for medical or physiological sensing applications (or equivalent with PhD)
Nice to Have
- Experience with contact-based and contactless vital sign measurement technologies
- Background working with medical-grade measurement devices and clinical reference standards
- Experience with embedded signal processing (ARM, edge devices)
- Knowledge of motion artifact removal and noise reduction in challenging environments
- Familiarity with DevOps practices for reproducible research (experiment tracking, data versioning)
- Background in medical devices, regulated environments, or clinical validation studies
