Lead Data and Signal Processing Engineer

Carbometrics Ltd; West of England; Vor Ort; GBP 60.000 - 70.000; Vollzeit

Zusammenfassung

Carbometrics Ltd in Bristol is seeking a Lead Data and Signal Processing Engineer to lead the pipeline turning raw optical sensor measurements into reliable analytical results.

You will develop algorithms, evaluate noise-removal methods, handle environmental factors, and work with scientists and engineers to translate observations into practical software.

This hands-on technical leadership role offers the chance to build a team as the company grows at Science Creates incubator.

Qualifikationen

• 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.

Aufgaben

• Architect and develop a robust pipeline converting raw sensor measurements into analytical outputs.
• Develop and evaluate filtering and noise-removal methods.
• Develop calibration, linearisation and conversion of sensor measurements.
• Assess performance across sensors, temperatures and sample conditions.
• Turn observations into practical algorithms and models with scientists and engineers.
• Support LabVIEW-based data-acquisition software and real-time visualisation.

Kenntnisse

Python

C/C++

LabVIEW

R

Data analysis

Visualization

Ausbildung

Physics degree

Engineering degree

Applied mathematics

Computer science

Tools

LabVIEW

R

Jobbeschreibung

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 investigation to a practical, implementable solution
• Experience in biomedical, medical device or in-vitro diagnostics development
• Strong problem-solving skills, working independently on technically ambiguous problems and collaboratively in a multidisciplinary environment
• Good data visualisation skills, communicating results clearly to scientists and engineers
• Excellent numeracy, literacy and computer skills, with strong attention to detail

Desirable skills and experience (not expected to have experience in all areas):

• Biosensor calibration, compensation or correction algorithms (temperature, matrix effects, cross-sensitivity, multi-analyte)
• Optical, electrochemical or other biosensing technologies
• Real-time data acquisition or instrument-control systems
• LabVIEW development or maintenance of research software, including graphical interfaces or dashboards for real-time measurements
• Statistical modelling, multivariate analysis or machine learning (regression, classification, anomaly detection)
• Time-series or high-frequency sensor data analysis
• Deploying algorithms into desktop, embedded or instrument-based production software
• Establishing reproducible data-processing, analysis or automated QC workflows

What’s On Offer

• The opportunity to establish and lead Carbometrics' signal-processing capability and build a team as the company grows
• A base at Science Creates, Bristol's leading scientific incubator, working alongside a motivated team of scientists
• An enjoyable environment at the frontier of glucose sensing, with room to grow your skills

Additional Information

• Candidates must be authorised to live and work in the UK, with respect for others and a commitment to high standards and continuous improvement.
• Closing date for applications is 29th October 2026, unless the role is filled earlier.
• Carbometrics values diversity and is an equal opportunities employer, welcoming applicants who meet the requirements of the role regardless of gender, ethnic origin, disability, age, religion or sexual orientation.

You may have experience in the following: Lead Signal Processing Engineer, Lead Algorithm Engineer, Sensor Data Scientist, Lead Data Scientist, DSP Engineer, etc