Senior Data Engineer

Most diseases are still poorly understood at a biological level. Despite decades of research, the causal mechanisms driving many conditions remain unclear, limiting our ability to identify the right targets, design the right interventions and bring the right medicines to patients. The AI Accelerator exists to change that. Based in London and sitting within Computational Innovation, a global organisation spanning computational biology, human genetics, data excellence and AI, the Accelerator’s mission is to build production-quality AI capabilities that deepen our understanding of disease biology and increase probability of success.

We do this by applying neural-based methods across the biomedical data landscape to integrate heterogeneous, multimodal data sources, infer biological relationships and embed causal thinking into what we build. The goal is not just to predict but to explain and understand why disease occurs. It could be electronic health records and medical imaging to support patient segmentation. It could be ‘omics data to identify novel therapeutic targets. It could be predicting transcriptional change for a given disease-causing variant. It could be simulating the effect of modulating a target of interest.

A core component of the AI Accelerator is AI Enablement, a team that provides the support framework to make our ambitions a technical reality. It could be provisioning integrated, multimodal biomedical data for model training and inference. It could be managing the lifecycle of models provided by AI Systems. It could be working with IT to ensure the right infrastructure and tooling are in place. AI Enablement ensures that the model builders can focus on the technology and that Computational Innovation’s downstream users can leverage accelerator capabilities for real portfolio impact.

We are seeking two Senior Data Engineers, one with a medical imaging focus and one with a multi-omics focus, to join the AI Enablement team and contribute to the design and delivery of robust data engineering pipelines that transform harmonised biomedical datasets into AI-ready, integrated assets across multi-omics, clinical and health records, and medical imaging data. You will be an experienced, independent data engineer within AI Enablement, owning significant data engineering workstreams within the broader technical direction and architecture set by the Senior Staff Data Engineer. The pipelines and integrated datasets you build will enable model training, fine-tuning and inference in a production setting.

Key Responsibilities

• Build and maintain entity linking pipelines that connect patients, samples and other biomedical entities across modalities such as imaging, clinical and multi-omics records.
• Build and maintain cross-modal integration pipelines that combine linked imaging, multi-omics and clinical records, each with different formats, scales and structures, into unified assets ready for multimodal model training, fine-tuning and inference.
• Ensure pipelines and datasets are built and operated in accordance with data access permissions, consent conditions and usage restrictions, including within Trusted Research Environments or other controlled access settings.
• Build and maintain biomedical benchmark datasets with versioning and documentation.
• Write clean, well-tested, well-documented code that meets the required engineering standards and contribute to code reviews within the data engineering team.
• Stay current with advances in data engineering tooling and practices relevant to biomedical AI.

Required Qualifications

• PhD in Machine Learning, Computer Science, Bioinformatics, Computational Biology or a related quantitative field.
• Strong hands-on experience in data engineering for machine learning with proficiency in modern data engineering tech stacks.
• Experience working with medical imaging modalities (radiology and/or histopathology) or multi-omics modalities (transcriptomics, proteomics) and a working knowledge of other biomedical data modalities sufficient to support cross-modal integration.
• Practical experience with entity linking or record linkage, ideally in a biomedical or clinical context, and a strong understanding of biomedical data characteristics such as variant data formats, expression matrices and clinical coding standards such as SNOMED and ICD-10.
• Familiarity with data governance frameworks applicable to biomedical and clinical data and Trusted Research Environments or controlled access biomedical data environments.

This is a hybrid role with approximately 3 days a week in the office.

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