Jobs 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.

Provide high-level administrative support to the Leadership Team: manage complex calendars, emails, travel, presentations, minutes, expenses, and improve administrative processes while maintaining confidentiality and strong stakeholder communication.

Lead the companywide data strategy, overseeing acquisition, integration, governance, and quality of diverse datasets (omics, imaging, clinical). Collaborate with engineering, data science, and scientific teams to build scalable data infrastructure, manage partnerships with biobanks and data providers, implement governance and compliance, and build a high-performing data strategy and acquisition team.

Lead statistical genetics and population genomics analyses to support target discovery. Design and implement post-GWAS methods (fine-mapping, colocalisation, MR, PRS, rare variant analyses), build scalable reproducible workflows, integrate genetics with multi-omics, partner with experimental and ML teams, communicate findings, and contribute to publications and decision-making across programmes.

Lead Relation's Data Platform team by designing a unified data strategy, optimizing data workflows, and ensuring data governance, while developing a high-performing engineering team.

Design, build, and maintain scalable cloud-native data pipelines and lake/lakehouse infrastructure for multi-modal scientific data. Optimize storage layouts and access patterns for analytics and GPU-based ML training, implement versioning, lineage, and quality monitoring, operate workflow orchestration and containerised environments, and collaborate closely with data scientists and ML researchers to enable efficient model training and research workflows while meeting security and governance needs.

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.

Operate end-to-end single-cell RNA and ATAC sequencing workflows, generate sequencing-ready libraries, ensure QC and metadata tracking in LIMS/ELN, collaborate with automation and computational teams to scale protocols and maintain SOPs and sample traceability.

Apply and develop statistical and ML methods for genomics and multi-omics data to support target discovery and validation. Build scalable, reproducible computational workflows, integrate human genetics with OMICs, collaborate with experimental and ML teams, communicate results, and contribute to scientific outputs.

As a Platform Engineer, develop and manage Kubernetes-based cloud infrastructure, enhance CI/CD pipelines, and collaborate with Machine Learning teams to optimize performance and reliability.

The job involves supporting computational drug discovery for antibodies, evaluating candidates, integrating insights, and collaborating with teams.