Applied Machine Learning Engineer

Applied Machine Learning Engineer Contract: Fixed-term until July 2029
Liverpool, hybrid (minimum 3 days on site per week)

Are you ready to push the boundaries of AI-driven sensing and digital diagnostics, contribute to technological innovation, and develop transformative tools for global health applications?

We’re looking for an Applied Machine Learning Engineer to join our multidisciplinary research and development team and play a key role in advancing intelligent healthcare sensor technologies within the Infection Innovation Technology Laboratory (iiTECH). You’ll develop and implement predictive algorithms and data models that enhance the analytical and decision-making capabilities of next-generation handheld and wearable sensing devices for the detection, monitoring, and prevention of infectious diseases.

You’ll contribute to the full innovation lifecycle, from data acquisition and model development to real-time implementation within embedded systems and clinical validation. You’ll work closely with engineers, biomedical scientists, clinicians and software developers to ensure predictive models are seamlessly integrated into sensor platforms for rapid and reliable health assessments.

Design, develop, and validate machine learning and statistical models for analysing multimodal sensor data
Optimise algorithms for deployment on embedded systems to support real-time health assessment.
Collaborate with electronics engineers to interface machine learning models with handheld and wearable sensor systems
Develop pipelines for real-time data acquisition and feature extraction and evaluate model performance and system-level integration
Establish rigorous data governance and pre-processing protocols to ensure data integrity, security, and compliance with healthcare standards
Work closely with partners in academia, industry, and global health organisations to align research objectives
Contribute to knowledge dissemination and impact by publishing and presenting research findings, supporting translation into practice through collaborations, providing training and helping to secure research funding

PhD or equivalent industrial experience in Computer Science, Data Science, Biomedical Engineering, Applied Mathematics, or a closely related discipline with a focus on machine learning or data-driven modelling
Proven expertise in developing, training, and validating machine learning and statistical models for predictive analytics and real-time data interpretation
Demonstrated ability to integrate ML algorithms with sensor systems, or embedded hardware
Proficiency in Python, MATLAB, or equivalent programming environments
Experience in data curation, feature engineering, and pre-processing for multimodal healthcare or sensor datasets
Strong track record of publishing research in peer-reviewed journals or writing industrial reports.
30 days annual leave, plus bank holidays, plus Christmas closure days
~ Generous occupational pension schemes
~ Government backed “cycle to work” scheme.
~ Affiliated, discounted staff membership to the University of Liverpool Sports Centre
~ A range of additional family friendly policies


Inclusion is central to our values at LSTM.
We seek to attract and recruit people who reflect the diversity across our communities, regardless of sexual orientation, gender identity, ethnicity, nationality, faith or belief, social background, age and disability. We welcome conversations about flexible working; Founded in 1898 and the oldest of its kind in the world, the Liverpool School of Tropical Medicine (LSTM) is an internationally recognised centre of excellence for teaching and research in tropical diseases. Through the creation of effective links with governments, NGOs, private organisations and global institutions and by responding to the health needs of communities, LSTM aims to promote improved health, particularly for people of the less developed/resource poorest countries in the tropics and sub-tropics.