Senior Wearable Biosignal DSP Scientist

lifelenstechnologies; Ivyland (PA); On-site; USD 110,000 - 160,000; Full time

Benefits offered by this job

Health Insurance

Dental & Vision Insurance

Paid Time Off

401(k)

Stock option incentive plan

FSA & dependent care

Job summary

LifeLens Technologies, Inc. in Ivyland, PA, is seeking a Senior Biomedical Signal Processing Scientist to lead algorithms for a multi-sensor wearable hub, including PPG, ECG, and accelerometry.

You will own algorithms from prototype to clinical validation and regulatory submissions, collaborating with hardware, firmware, and clinical teams. The role covers offline analysis of large datasets and real-time device implementations, with mentoring of junior engineers and occasional travel to support

Qualifications

• PhD or higher in a quantitative field with relevant signal processing research.
• Experience with wearable biosignals (PPG/ECG) and sensor fusion.
• Strong programming and debugging skills; able to write clean, reproducible code.

Responsibilities

• Design and validate algorithms deriving physiological measurements from multiple sensors (PPG, ECG, accelerometry).
• Conduct offline analyses of large clinical/field datasets to characterize signal behavior and algorithm performance.
• Adapt validated algorithms for real-time device use with firmware engineers.
• Produce verification/validation evidence to support regulatory submissions; mentor junior team members.

Skills

Digital signal processing

Python

MATLAB

Statistical analysis

Machine learning concepts

Signal quality assessment

Communication skills

Education

PhD in Biomedical engineering or related field

MS in related field

BS in related field

Tools

MATLAB

C

Job description

LifeLens Technologies, Inc. in Ivyland, PA, is seeking a Senior Biomedical Signal Processing Scientist to lead algorithms for a multi-sensor wearable hub, including PPG, ECG, and accelerometry.

You will own algorithms from prototype to clinical validation and regulatory submissions, collaborating with hardware, firmware, and clinical teams. The role covers offline analysis of large datasets and real-time device implementations, with mentoring of junior engineers and occasional travel to support