Senior Machine Learning Scientist (Single Cell)

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About the role

About Relation Relation is a sector defining TechBio company developing transformational medicines, with technology at our core. Our ambition is to understand human biology in unprecedented ways, discovering therapies to treat some of life’s most devastating diseases. We leverage single-cell multi-omics from patient tissue, functional assays, and machine learning to drive disease understanding, from cause to cure. We are scaling rapidly and building a team of exceptional individuals to push the boundaries of drug discovery. You will work in highly interdisciplinary teams where biology, computation, and engineering come together to solve complex problems that have not been solved before. Our state-of-the-art wet and dry labs in the heart of London are designed to accelerate this integration and translate insight into impact. We are committed to building diverse and inclusive teams. Relation is an equal opportunities employer and does not discriminate on the basis of gender, sexual orientation, marital or civil partnership status, gender reassignment, race, colour, nationality, ethnic or national origin, religion or belief, disability, or age. By joining Relation, you will help define how medicines are discovered and deliver meaningful impact for patients.   The opportunity We are seeking an exceptional Machine Learning Scientist who combines strong ML fundamentals with a deep understanding of biological data, to help build the next generation of generative and predictive models of cellular behaviour. Your work will be central to our mission to understand and control cellular decision-making, enabling novel therapeutic strategies grounded in generative models. You'll be joining a team with access to cutting-edge multiomic and interventional datasets, advanced computational infrastructure, and deep interdisciplinary expertise. We embrace modern ML tooling, including agentic workflows, to accelerate the pace of research iteration. This is an opportunity to push the boundaries of what generative modelling can achieve in complex, high-dimensional, and noisy real-world systems, and to see your work tested directly in experimental biology.   Day to day, you will • Design and implement generative modelling approaches that learn intervention effects from diverse biological data, including single-cell perturbation experiments. • Develop models that go beyond correlation, focusing on generalisation, counterfactual prediction, and experimental design. • Collaborate with experimental teams to design and validate computational hypotheses via iterative strategies that identify the highest-signal next experiment. • Evaluate models not just for fit, but for causal coherence, mechanistic fidelity, and utility in guiding real-world interventions. • Communicate findings clearly to colleagues and stakeholders from different disciplines.   Professionally, you will have • A PhD in machine learning, computer science, statistics, or a related quantitative field. • Strong methodological foundations in modern ML, with depth in at least one area relevant to modelling structured, high-dimensional data. • Excellence in Python and familiarity with scalable ML tooling and high-performance computing. • Strong engineering practice: confidence implementing models from scratch, comfortable with distributed training, profiling, and performance optimisation. • Demonstrable experience training models at scale, meaningfully scaling architectures or training schemes. • A track record of moving from idea to working scaled implementation: adapting or designing models that respect the data, rather than applying off-the-shelf methods. • Comfort working with messy, noisy, real-world scientific data. Bonus experience: • Development of widely-adopted tools or meth

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