Machine Learning Engineer

About This Role

This Machine Learning Engineer role focuses on developing next-generation models for longitudinal microbiome systems within the life sciences. It involves creating new computational methodologies and translating research into reproducible scientific technology, rather than simply applying standard machine-learning pipelines. The position is suited for researchers with expertise spanning computational biology and machine learning.

Responsibilities

• Develop computational models for microbiome dynamics, perturbation, recovery, resilience, and broader biological network problems
• Build biologically meaningful graph and multi-omics representations that integrate microbial, genomic, metabolic, pathway, phylogenetic, and host information
• Formulate biologically meaningful questions and translate them into rigorous computational experiments
• Design and evaluate GNN, graph-transformer, temporal, causal, and interpretable learning methods
• Address microbiome-specific challenges such as compositionality, sparsity, batch effects, confounding, and irregular sampling
• Establish strong classical and statistical baselines and distinguish statistical associations from biologically plausible mechanisms
• Translate state-of-the-art research into reproducible experiments, research software, technical reports, and peer-reviewed publications
• Collaborate across computational and experimental teams and with academic, scientific, and industry partners
• Develop reusable methods and technologies rather than one-off analyses wherever possible

Requirements

• A PhD, or a research-intensive MSc with significant experience, in Computer Science, Machine Learning, Bioinformatics, Computational Biology, Microbiology, Genetics, Genomics, Systems Biology, or a closely related discipline
• A strong background in statistics and machine learning, with the ability to design, train, validate, and rigorously benchmark predictive models for complex scientific datasets
• Practical experience developing models in Python using PyTorch, JAX, TensorFlow, or an equivalent framework
• Experience with graph machine learning or biological network modelling (GNNs, graph attention or transformers, heterogeneous graphs, knowledge graphs, or temporal graph models)
• Strong working knowledge of microbiology, microbial ecosystems, genetics, molecular biology, or bioinformatics, enabling effective communication with biological collaborators
• Experience analysing sequencing or omics data such as 16S, metagenomics, microbial genomics, transcriptomics, or metabolomics
• The ability to formulate biologically meaningful computational questions, design rigorous experiments and baselines, and communicate across computational and experimental disciplines

Nice to have: