Machine Learning Engineer

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.

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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)