Job Title: Machine Learning Engineer – Biotech / Life Sciences Location: Cambridge (Hybrid) Salary: £75,000–£90,000 benefits About Us One of our favourite clients is a pioneering data-driven discovery in biotechnology.
Hit Apply below to send your application for consideration Ensure that your CV is up to date, and that you have read the job specs first.
Founded by a team of computational biologists, ML engineers, and pharma veterans, we’re building a next-generation platform that accelerates therapeutic target discovery using large-scale biological data, machine learning, and advanced statistical modelling.
Based in the heart of Cambridge’s biotech cluster , we’ve recently secured our Series A funding, built strategic partnerships with top-10 pharma companies, and are growing our interdisciplinary team.
We’re now looking for a Machine Learning Engineer to help us solve some of the most exciting problems at the intersection of AI and life sciences — from understanding gene-disease relationships to modelling cell behaviour and predicting drug response.
Day to Day: Design, train, and deploy machine learning models using high-dimensional biological datasets (e.g.
RNA-seq, single-cell, proteomics, CRISPR screens) Build and maintain scalable ML pipelines that integrate with our internal data platforms Collaborate with wet-lab scientists, bioinformaticians, and software engineers to translate research hypotheses into data-driven models Apply techniques including representation learning , graph neural networks , multi-modal learning , and Bayesian optimisation Focus on model interpretability, uncertainty quantification, and reproducibility in scientific contexts Stay current with ML/AI developments in biotech, and continuously explore new methods to improve predictive performance and biological insight Background: Solid software engineering skills in Python , with strong knowledge of machine learning libraries such as scikit-learn, PyTorch, TensorFlow, XGBoost , etc.
Previous experience applying ML to complex scientific or biological datasets Familiarity with biological data types (e.g., omics data, imaging, assay data, gene expression, pathway data) Experience building reproducible, production-grade data pipelines (e.g., Airflow , MLflow , Docker ) Strong understanding of statistics, experimental design , and model validation Ability to collaborate across disciplines — from data scientists and software engineers to domain scientists and lab researchers Nice to Have: Experience with single-cell analysis , genomics , or biomarker discovery Familiarity with biological ontologies (e.g., Gene Ontology, Reactome, Ensembl) Knowledge of Bayesian methods , causal inference , or generative modelling in a scientific setting Exposure to graph-based learning (e.g., knowledge graphs, protein interaction networks) Experience in cloud-based ML workflows (GCP, AWS, or Azure) Prior startup or scale-up experience in a biotech or healthtech environment Why Join Us? Work on problems that genuinely matter — advancing drug discovery and human health A collaborative, mission-driven team at the cutting edge of biotech and AI Competitive salary and meaningful equity package 25 days holiday bank holidays Christmas shutdown Private healthcare & wellbeing allowance Annual learning/conference budget and access to leading academic collaborators Modern office and lab space in central Cambridge (with a fantastic coffee machine)
