Senior/Principal Machine Learning Scientist (Generative Modelling)

Relationrx · Londonsenior

En corto

▸Desarrollar modelos generativos avanzados para predecir respuestas celulares a intervenciones biológicas usando datos de alta dimensión.
▸Trabajar en equipo interdisciplinario (biología, computación, ingeniería) para validar hipótesis con experimentos reales en laboratorio.
▸Destacar por la aplicabilidad de los modelos a problemas reales de descubrimiento de fármacos, con retroalimentación directa desde el laboratorio.

Proficiency in English required to collaborate across global, interdisciplinary teams.

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¿Qué piden?

✓Doctorado en IA, estadística, ciencia de cómputo o campo cuantitativo relacionado.
✓Experto en modelos generativos, especialmente con datos biológicos complejos.
✓Fundamentos sólidos en modelado probabilístico o aprendizaje de representaciones.
✓Excelente competencia en Python y entornos de computación de alto rendimiento.
✓Experiencia en evaluación de modelos más allá del ajuste, enfocándose en coherencia causal y utilidad práctica.
✓Experiencia previa con datos de célula única o experimentos de perturbación es un gran plus.

¿No cumplís todo? Es lo normal — tu dossier gratis te dice qué gaps tenés y cómo cubrirlos en la entrevista.

Generative modellingProbabilistic modellingRepresentation learningNeural network architecturesSingle-cell multi-omicsFunctional assaysAgentic workflowsPythonHigh-performance computingInterdisciplinary collaboratio

¿A quién escribirle en Relationrx?

Tu dossier gratis identifica a las personas que te entrevistarían — con su background, qué valoran y cómo escribirles para destacar antes de aplicar.

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 Relation is offering an outstanding opportunity for a Machine Learning Scientist 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 PhD in ML, statistics, computer science, or a related quantitative field. Deep expertise in generative modelling. Strong foundations in probabilistic modelling, representation learning, or neural network architectures for structured or sequential data. Excellence in Python and familiarity with scalable ML tooling and high-performance computing. A disciplined approach to model evaluation, with experience designing experiments that go beyond standard benchmarks to test real-world utility. Willingness and ability to engage deeply with biological data; prior experience with single-cell or perturbational datasets is a strong plus. Bonus experience : Track record of impactful publications or open-source contributions in ML. Experience working in interdisciplinary teams or applying ML in real-world settings. Personally, you Are comfortable working in a matrixed environment, balancing multiple stakeholders and contributing effectively across teams. Take ownership of your work , proactively seek opportunities to contribute, and enable others to do their best work.

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