Senior Machine Learning Scientist (Single Cell)

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.

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.

• 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., * 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.

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!

• 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 methods in the single-cell ML ecosystem.
• High-impact publications at the intersection of ML and biology.
• Experience with perturbational or interventional datasets (e.g. Perturb-seq, CRISPR screens).