Signal Processing Engineer

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 (FDA-cleared for pulse and respiratory rate, more in the pipeline), running assessments, documenting to the EMR, and escalating when something's wrong. Autonomously.

We're not building demos. We're deployed in real facilities today, monitoring hundreds of patients daily. We're solving one of healthcare's hardest problems: a global nursing shortage that will hit 40% by 2030.

We're a small, international team backed by top-tier VCs, with offices in Brooklyn and Paris. We ship things that matter.

We're looking for a Signal Processing Engineer to join our algorithm and development team, working on remote vital sign monitoring from video. You'll develop and validate algorithms that extract clinical-grade physiological signals (heart rate, blood pressure, respiration) from camera data, working closely with computer vision engineers in our algorithm team.

• →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

• 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 (Radar, 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)
  

• Experience with contact-based and contactless vital sign measurement technologies especially radar
• 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 studie