Machine Learning Research Engineer

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 Senior Machine Learning Research Engineer to help build and scale the next generation of generative and predictive models of cellular behaviour. Research Engineers at Relation are software engineers with a deep understanding of machine learning and deep learning, acting as a critical bridge between theory and implementation: designing, building, and scaling the complex systems on which our ML research depends. You'll be joining a team with access to cutting-edge multiomic and interventional datasets, advanced computational infrastructure, and deep interdisciplinary expertise, and a culture that embraces modern ML tooling, including agentic workflows. Key responsibilities include implementing and optimising large neural network models; building robust infrastructure for distributed training, evaluation, and inference; and partnering with ML Scientists to take research from prototype to production-grade systems applied to large, multi-modal biological data, tested directly in experimental biology.

Day to day, you will

• Implement and optimise large models, partnering with ML Scientists to translate research ideas into reproducible, scalable training pipelines.

• Profile and optimise training across compute, memory, and I/O, pursuing measurable gains in throughput, convergence, and stability.

• Design and implement distributed training strategies across multi-GPU and multi-node configurations.

• Build and maintain core ML infrastructure.