ML Engineer for Drug Safety Side-Effect Resources

European Bioinformatics Institute; Cambridgeshire and Peterborough; Hybrid; GBP 39,000 - 43,000; Full time

Benefits offered by this job

Private medical insurance

Hybrid working patterns

30 days annual leave

Job summary

EMBL-EBI seeks a scientific developer to advance the Safety 2.0 project. You will implement machine learning and data processing pipelines to extract side effect data from drug labels, trials and literature, and map results to ontologies.

You will collaborate across multidisciplinary teams and contribute to open source resources for the global research community. The role sits within the Chemical Biology Resources team on the Wellcome Genome Campus, offering hybrid work patterns and access to

Qualifications

• PhD or equivalent in cheminformatics, bioinformatics or related field.
• Strong Python programming skills and data processing experience.
• Experience with biomedical knowledge extraction and standardisation.

Responsibilities

• Develop ML pipelines to extract safety data from diverse sources.
• Map extracted data to biomedical ontologies and integrate with Open Targets/ChEMBL.
• Collaborate across Safety 2.0, Open Targets and Chemical Biology Resources teams.
• Contribute to open source code and public resources with robust documentation.

Skills

Python

PhD in cheminformatics / bioinformatic

Attention to detail

Communication skills

Knowledge extraction / data standardis

Education

PhD in cheminformatics or bioinformatics

Tools

Git

Ontology mapping

Job description

EMBL-EBI seeks a scientific developer to advance the Safety 2.0 project. You will implement machine learning and data processing pipelines to extract side effect data from drug labels, trials and literature, and map results to ontologies.

You will collaborate across multidisciplinary teams and contribute to open source resources for the global research community. The role sits within the Chemical Biology Resources team on the Wellcome Genome Campus, offering hybrid work patterns and access to