What you will do

Collaborate on designing and implementing new data infrastructure and pipelines preparing data for large-scale ML workflows
Care about data quality, and ensuring the pipelines you build are robust, scalable, and maintainable
Work with DICOM data to feed into foundation model and disease-specific imaging model development
Collaborate closely with Machine Learning Scientists, DevOps Engineers, and other Data Engineers to create a tight feedback loop and ensure the end-to-end process is effective and efficient
Ensure that our data processes have quality and compliance designed in from the start to make reproducibility, lineage tracking, and data quality painless
Scale pipelines to handle millions of scans – ingesting the imaging data, transforming it, filtering and structuring ready for foundation model development.

What we need...

Proven experience as a Data Engineer in complex, data-rich environments
Strong programming skills in Python
Experience building and maintaining production ML data pipelines, including orchestration tools such as Dagster and cloud infrastructure on AWS
Experience with Docker and Kubernetes based infrastructure Experience working with large datasets
Understanding of data preprocessing and quality control for machine learning
Strong collaboration skills with machine learning or technical teams

Even better if you have experience of...

Medical imaging data such as CT, MRI, or DICOM
Large-scale datasets or foundation model workflows
Deployment tooling (Helm and familiarity with Gitops tooling such as Flux and Kustomize)
Data versioning and reproducibility frameworks
Database design and data modelling
Working in regulated or GxP or ISO 13485 environments
Experience with ML experiment tracking or metadata management (MLFlow)

A comprehensive benefits package that includes an annual bonus plan, private medical insurance, life insurance, and a contributory pension scheme
25 days annual leave, plus bank holidays and enhanced maternity leave
A diverse work environment that brings together experts in many fields, including software engineering, devops, data science, machine learning, quality assurance, regulatory affairs, and clinical operations.

Data Engineer – Science

Key skills