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About the Company This BioAI startup is developing next-generation diagnostic technologies for bloodstream infections using cutting-edge machine learning and DNA sequencing. The team combines expertise across genomics, microbiology, and data science to accelerate how infectious diseases are detected and treated. Having built a strong foundation in both lab and data infrastructure, the company is now expanding its compsci team with a focus on developing advanced ML models for genomic analysis – work that directly contributes to saving lives through faster, more accurate diagnosis. The Role We’re hiring a Machine Learning Engineer to lead the development of a bacterial genome anomaly detection system – building bespoke algorithms that identify unusual patterns in genomic data and support the company’s mission to prevent incorrect antibiotic prescriptions. You’ll design and test novel ML methods using foundational pre-trained genomic embeddings and custom anomaly-detection architectures , turning proprietary data into interpretable, high-impact models. This is a deep research role: success will come through rapid iteration, creativity, and scientific curiosity rather than polished productisation. It’s well suited to someone who thrives in a small, autonomous team, enjoys experimental algorithm development, and wants their work to have measurable real-world impact. What You’ll Do • Design and implement bespoke anomaly-detection models for bacterial genomes • Develop, train, and benchmark transformer-based and foundation-model approaches for genome representation • Conduct rapid, iterative research , evaluating ideas through experiments rather than long production cycles • Collaborate with bioinformatics, microbiology, and software teams to integrate models into GenomeKey’s diagnostic pipeline • Analyse large-scale proprietary genomic datasets to ensure model robustness and interpretability • Generate and evaluate synthetic and real-world data for validation • Ship prototype code to third-party partners for testing and feedback • Contribute to broader R&D initiatives such as statistical framework design and data infrastructure development What We’re Looking For Required • MSc or PhD in Machine Learning, Computational Biology, Bioinformatics, or related discipline (or equivalent industry experience) • Demonstrated ability to apply ML methods to biological or genomic data • Strong Python skills with experience in PyTorch, TensorFlow, or scikit-learn • Understanding of bioinformatics workflows (e.g. genome assembly, QC, annotation) • Experience working with large or complex genomic datasets • Familiarity with model evaluation, benchmarking, and explainability • Ability to work autonomously, design experiments, and iterate quickly • Strong communication skills for cross-functional collaboration Why Join? • Work on a genuinely novel problem – genomic anomaly detection for clinical diagnostics • Combine academic-level research with startup agility and real-world impact • Autonomy to explore and build new ML algorithms from first principles • Join a collaborative, science-driven team that values experimentation and creativity • Contribute to technology that could change how bacterial infections are diagnosed worldwide Apply for this role: Share this role: Similar Opportunities Product Senior Recruitment Consultant – AI Product & Programme (UK) Grow a specialist recruitment desk across product and programme hiring within the UK AI market. Artificial Intelligence Internal Manchester Software Platform Engineer A rapidly scaling biotech company leveraging AI and large-scale biological data to drive discovery across genomics, therapeutics, nutrition, and agriculture. Artificial Intelligence London Product Technical Program Manager By combining a proprietary multi-billion-gene dataset with advanced AI and machine learning, this firm is redefining what is possible in genomics, therapeutics, nutrition, and agriculture. Artificial Intelligence London Software Software Engineer Join this fast-growing London-based biotech operating at the cutting-edge of AI research and biological discovery.