Senior Machine Learning Scientist - Cellular Modelling
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 Senior Machine Learning Scientist who combines strong ML fundamentals with a deep understanding of biological data. You will develop machine learning approaches that are purpose-built for the structure and complexity of single-cell and multiomic datasets. The goal is to understand how cells respond to interventions, and to translate that understanding into therapeutic strategy. You'll work within a team that sits at the intersection of generative modelling and experimental biology, interrogating interventional datasets and working in close collaboration with wet-lab scientists.
Day to day, you will
Design and implement ML models that operate on single-cell and multiomic data, with careful attention to the biological structure of these datasets.
Develop representation learning, probabilistic, or structured prediction approaches for modelling cellular state and its response to perturbation.
Contribute to shaping the team's research roadmap on cellular modelling.
Work closely with experimental teams to translate biological questions into well-posed modelling problems, and to interpret model outputs in biologically meaningful terms.
Contribute to rigorous model evaluation, going beyond standard reconstruction metrics to assess whether models capture biologically coherent structure.
Stay current with the rapidly evolving landscape of ML for single-cell biology and bring new ideas into the team.
Communicate findings clearly to colleagues and stakeholders from different disciplines.
Professionally, you will have
A PhD in machine learning, computational biology, statistics, or a related quantitative field.
Hands-on experience building ML models for biological data, ideally single-cell transcriptomics, multiomics, or perturbational datasets.
Strong methodological foundations in modern ML, with depth in at least one area relevant to modelling structured biological data (e.g. probabilistic modelling, representation learning, geometric deep learning).
Fluency in Python and modern ML frameworks (PyTorch, JAX, or similar), with experience working at scale.
A track record of bridging ML methodology and biological application - not just applying off-the-shelf methods but adapting or designing models that respect the 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).
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.
Relation is a committed equal opportunities employer.