Teams at Relation Therapeutics

Develop and evaluate machine learning models linking DNA sequence and genetic variation to molecular phenotypes and disease. Build sequence-to-function models (variant effect and regulatory activity prediction), integrate sequence representations with transcriptomic and epigenomic data, collaborate with experimental teams to validate hypotheses, communicate results, and contribute to publications and documentation.

Lead Relation’s Platform Engineering team and technical strategy for hybrid on-premises and cloud infrastructure supporting machine learning, scientific computing, genomic pipelines, and internal services. Own Kubernetes architecture, MLOps environments, CI/CD, developer tooling, observability, disaster recovery, platform security, and governance. Hire and develop engineers, guide technical direction, manage production operations and incidents, and partner with Engineering, Data Science, and Machine Learning stakeholders.

Develop and apply sequence‑modelling ML techniques to DNA to interpret variants, discover genes, and model regulation. Train, fine‑tune and evaluate models, collaborate with computational and experimental teams, leverage large-scale datasets, design robust biological evaluations, and integrate ML advances into translational target discovery.