Signal Processing Engineer

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Job type: Permanent

County: Leicestershire

Country: United Kingdom

Salary/rate: £65,000 to £75,000

Discipline: Engineering

Job ref: CT2035

Post Date: 16-05-2026 09:40 AM

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Cure Talent are delighted to be partnered with an emerging wearable medical technology company at a defining stage of its growth. Developing next-generation physiological monitoring solutions designed to deliver medical-grade insights beyond the hospital environment, the business is advancing its sensing and data science capabilities to enable continuous monitoring of key physiological parameters across a range of care settings.

We have an opportunity for a Signal Processing Engineer to join the Data Science team and support the development of algorithms and modelling techniques used to interpret complex physiological signals collected from non-invasive wearable sensors.

As the Signal Processing Engineer, you will design signal processing pipelines and analytical models capable of extracting clinically meaningful cardiorespiratory and haemodynamic metrics from real-world sensor data. The work sits at the intersection of signal processing, physiological modelling and applied data science, translating advanced sensing technologies into reliable clinical insights.

This is a hands-on position working closely with data science, engineering and clinical teams to develop and refine algorithms that underpin the organisation’s wearable monitoring technology. The role will also contribute to experimental design and the analysis of datasets generated through internal testing and controlled clinical studies.

To be successful as the new Signal Processing Engineer, you will bring strong experience in signal processing and analysis of physiological or time-series data. You will be comfortable developing algorithms or modelling techniques used to interpret complex biosignals and capable of translating theoretical approaches into practical implementations that support product development.

Key Responsibilities

• Design and implement signal processing pipelines to analyse non-invasive physiological signals collected from wearable sensors.
• Develop algorithms and modelling techniques to extract clinically meaningful metrics related to cardiorespiratory and haemodynamic function.
• Analyse complex physiological time-series data including signals such as ECG, PPG and other non-invasive biosignals.
• Support the design of experiments and analyse data generated through internal testing and controlled clinical studies.
• Work closely with Data Science and Engineering teams to integrate algorithms into software systems and product architectures.
• Collaborate with clinical and product teams to ensure algorithms are aligned with physiological understanding and clinical requirements.
  
  

Experience And Skills Required

• Demonstrated experience in signal processing and analysis of physiological or time-series data.
• Strong understanding of signal processing techniques including filtering, noise reduction and feature extraction.
• Experience developing algorithms or modelling techniques used to interpret physiological signals.
• Experience working with non-invasive physiological signals, particularly cardiorespiratory or haemodynamic signals.
• Programming experience using environments commonly used for signal processing and modelling such as MATLAB, Python or similar high-level languages.
• Experience designing experiments and analysing data generated through controlled studies or experimental testing.
• Bachelor’s degree in Electrical Engineering, Biomedical Engineering, Computer Science, Physics, Applied Mathematics or a related quantitative discipline is required. A Master’s or PhD in a relevant field would be advantageous.
  
  

This is a high-impact opportunity within a growing wearable MedTech business where signal processing and physiological modelling are central to the development of next-generation monitoring technologies. It will suit an engineer or scientist who enjoys solving complex signal challenges and contributing directly to the translation of innovative sensing technologies into clinically meaningful insights.

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