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About the Role
The AI Accelerator aims to deepen the understanding of disease biology and increase the probability of success in developing new medicines by building production-quality AI capabilities. This role will contribute to the design and delivery of robust data engineering pipelines that transform harmonized biomedical datasets into AI-ready, integrated assets.
Responsibilities
- Build and maintain entity linking pipelines that connect patients, samples, and other biomedical entities across modalities.
- Build and maintain cross-modal integration pipelines that combine linked imaging, multi-omics, and clinical records into unified assets 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.
- Build and maintain biomedical benchmark datasets with versioning and documentation.
- Write clean, well-tested, well-documented code that meets required engineering standards and contribute to code reviews.
- Stay current with advances in data engineering tooling and practices relevant to biomedical AI.
Requirements
- 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).
- 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.
- Strong understanding of biomedical data characteristics such as variant data formats, expression matrices, and clinical coding standards (e.g., SNOMED, ICD-10).
- Familiarity with data governance frameworks applicable to biomedical and clinical data and Trusted Research Environments or controlled access biomedical data environments.
Skills
- Data Engineering
- Machine Learning
- Medical Imaging
- Multi-omics
- Entity Linking
- Record Linkage
- Biomedical Data
- Clinical Data
- Data Governance
- Trusted Research Environments
Location
- London
Work Type
- Hybrid
- 3 days a week in the office
Experience Level
- Senior
Education Level
- PhD
About the Company
- The AI Accelerator is part of Computational Innovation, a global organization spanning computational biology, human genetics, data excellence, and AI.
- Boehringer Ingelheim has been recognized as a Top Employer in the UK.