Lead Data and Signal Processing Engineer

Lead Data and Signal Processing Engineer

Salary: £60,000 – £70,000, depending on experience

Location:

Science Creates St Philips, Bristol

Working pattern:
Full time, 37.5 hours per week, on-site

Expected start date: ASAP

About Us

Carbometrics is a Bristol-based chemistry company designing highly selective binding molecules for sensors used in hospitals, medical devices, bioprocessing and industry. Our mission is to help people with diabetes and other conditions live longer, more comfortable lives through improved real-time monitoring of key analytes such as glucose.

Built on over 20 years of research from Professor Anthony Davis' group at the University of Bristol, our Biomimetic Glucose Binding Molecules (GBM) underpin a glucose sensing platform enabling a market-leading Continuous Glucose Monitor (CGM). The same technology, developed through our predecessor company Ziylo, was acquired by Novo Nordisk for smart insulins. We have since added potassium, lactate, pH, CO₂ and calcium, building toward the most comprehensive continuously monitored sensor array for medical use.

Role Overview

We are searching for a Lead Data and Signal Processing Engineer to lead development of the pipeline that converts raw optical sensor measurements into reliable analytical results.

You will develop algorithms from initial investigation of experimental data through to implementation in product and instrument software, separating sources of variability such as noise, environmental effects, sensor-to-sensor variation and transient artefacts.

You will also develop tools for analysing our growing calibration and QC datasets and improve how data is visualised. The role focuses on algorithms, not full product software or user interface ownership.

This is a hands-on technical leadership role. It does not initially carry line-management responsibility, but as the company grows there will be an opportunity to build and lead a team.

What You'll Be Doing

Core responsibilities:

Architect and develop a robust pipeline converting raw sensor measurements into reliable analytical outputs

Develop and evaluate filtering and noise-removal methods, separating signal from instrument noise and artefacts

Develop signal correction and bias compensation for temperature, dilution, pH, sensor variability and other environmental or physiological factors

Develop methods for detecting changes, transitions and abnormal behaviour, including change-points, bubbles and flow artefacts

Develop calibration, linearisation and conversion of sensor measurements into analyte concentrations

Assess the performance and limitations of each pipeline stage and the complete system, characterising performance across sensors, concentrations, temperatures and sample conditions

Use experimental data to decide which correction approaches are appropriate, rather than one model for unrelated sources

Turn experimental observations into practical algorithms and models, working with scientists and engineers

Establish reproducible approaches to data processing and algorithm development, and take technical ownership of the data and signal-processing aspects of sensor development

Additional responsibilities:

Support development of software interfaces and LabVIEW-based data-acquisition software for operating and monitoring instrumentation and sensors

Develop real-time visualisation of measurements and system status for demonstrations, partner evaluations and experiments

Investigate practical applications of statistical modelling and machine learning on existing calibration, QC and experimental data

Support the wider team in using data-analysis, software and AI-assisted tools

Contribute to experimental design to help distinguish between competing hypotheses

Help build the technical capability, tools and processes for robust sensor data analysis as the company grows

What You'll Bring

Qualifications:

Essential:
Degree in physics, engineering, applied mathematics, computer science or a related technical discipline

Desirable:
PhD or equivalent research experience in signal processing, sensing or machine learning

Essential skills and experience:

Minimum 4 years' industrial experience of signal processing, algorithm development or quantitative analysis of sensor or instrument data

Expert Python programmer (C/C++, LabVIEW or R advantageous)

Experience treating noisy or imperfect measurement data to improve reliability

Experience developing mathematical, statistical or computational models from raw data, taking problems from initial…