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. 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)