Machine Learning Scientist, Generative Modelling in London

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.

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.
• Communicate openly and directly, give and receive feedback constructively, and handle challenging conversations with respect.
• Actively seek out diverse perspectives, build strong working relationships, and contribute to shared goals across teams.
• Embrace challenges with openness and resilience, set high standards for yourself, and strive to deliver meaningful outcomes.

Working Style & Culture at Relation

At Relation, we operate in a matrixed, interdisciplinary environment, where impact is driven through collaboration across scientific, technical, and operational domains. We collaborate, and you will partner with colleagues across multiple teams and projects, contributing your expertise while aligning to shared company priorities. We work together and win together!

The patient is waiting!

Recruitment Agencies

Please note that Relation does not accept unsolicited resumes from agencies. Resumes should not be forwarded to our job aliases or employees. Relation will not be liable for any fees associated with unsolicited CVs.