Machine Learning Scientist, Generative Modelling

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.