Digital Signal Processing Engineer

Compensation

Commensurate with qualifications and experience

Background

Healthsign is a medical technology company headquartered in Ottawa, Ontario, Canada. We conduct cutting-edge R&D to develop innovative IoT-enabled non-invasive remote health monitoring devices. We have recently developed the patented Healthsign smart wristband, which is the first remote wearable medical device that conveniently, continuously, and accurately monitors and alerts for key physiological parameters including cuffless non-invasive blood pressure (NIBP), blood oxygen saturation (SpO2), temperature, respiration, and heart rate.

Job Description

Healthsign is looking to hire a dynamic and energetic digital signal processing (DSP) engineer to work on developing data analysis algorithms for the Healthsign smart wristband. You will be working with a multidisciplinary team of engineers, scientists, and clinicians to bring the Healthsign smart wristband technology to fruition. You will build innovative and robust DSP algorithms and add more functionality to the existing Healthsign DSP algorithms that analyze physiological waveform data to assess vital signs like NIBP and SpO2.

Key Responsibilities

• Developing innovative and robust DSP algorithms.
• Writing DSP code using programming languages like MATLAB and C.
• Reviewing and improving existing DSP algorithms and code.
• Maintaining and documenting all DSP code.
• Profiling and debugging DSP code and optimizing the code for speed, performance, and robustness.
• Participating in R&D for DSP algorithm design, development, documentation, and testing to deliver a best-in-class DSP software system.

Skills and Qualifications

• Degree in electrical engineering, computer science, or equivalent education and/or experience.
• Proven experience as a DSP engineer (either coursework or work experience).
• Proficient in MALTAB, C, and/or related signal processing programming languages.
• Good knowledge of time and frequency domain analyses like fast Fourier transforms (FFTs) and wavelets.
• Experience of developing digital filtering algorithms using infinite impulse response (IIR) and finite impulse response (FIR) filters for cleaning noisy signals.
• Experience of performing statistical analyses on data and waveform signals.
• Good knowledge of the concept of signal to noise ratio (SNR) and estimating SNR in noisy signals.
• Good knowledge of the sampling theorem and its application to data acquisition and signal analysis.
• Experience with modelling and running simulations with MATLAB.
• Basic knowledge of artificial intelligence (AI) and machine learning (ML) techniques pertinent to data and signal analysis will be a bonus.