Signal Processing Engineer

About the Company

Norbert is building autonomous robots that deliver healthcare. Our AI sensing platform mounts on mobile robots and does the work of a care team member—rounding on patients, capturing vitals without contact, running assessments, and documenting to the EMR.

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 video-based measurements against clinical reference devices (ECG, PPG, BP monitors)
• Design studies to ensure millisecond-level synchronization between video streams and reference sensors
• Integrate with the computer vision pipeline using C++
• Architect cloud-based QA and automated verification systems for algorithm validation
• Prototype algorithms in Python and C++ before production deployment

Requirements

• Master's degree or PhD in Electrical Engineering, Biomedical Engineering, or a related field
• 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 and C++
• Experience with spectral analysis, adaptive filtering, and time-domain methods
• 5+ years of industry experience in signal processing for medical or physiological sensing applications

Preferred Qualifications

• 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
• Familiarity with DevOps practices for reproducible research
• Background in medical devices, regulated environments, or clinical validation studies

Benefits

• Real impact on patient care
• High autonomy and technical ownership
• Equity participation in the company's future
• Work at the intersection of AI, edge computing, and healthcare