Bio/ML Intern (3-6 months)
London
Vacancy listed 19/02/2026
Application deadline 31/03/2026
Details

At Sable Bio we are working on a hard problem: trying to anticipate safety issues for drug targets to reduce failure rates, and bring safer medicines to patients.

What we're looking for: 

We are seeking a motivated Bio/Machine Learning intern to join our team working at the interface of biology and data science. This paid internship offers hands-on experience in applying computational, statistical, and machine-learning methods to solve real-world problems in drug discovery and target safety assessment.

As an intern, you will take ownership of a defined project within the platform, shaped around your background and interests, with the support of a multidisciplinary team. 


What you’ll do:

  • Analyse and integrate biological datasets (e.g.clinical data, functional genomics, mechanistic data) to support target safety assessment
  • Develop or extend computational pipelines for processing, validating, and analysing biomedical data for use in Sable platform models and features
  • Apply statistical or machine-learning methods to quantify and prioritise biological risks
  • Interpret results in a biological and translational context, with a focus on supporting drug discovery decision-making
  • Collaborate with software engineers to turn analyses into Sable platform features, used directly by by toxicologists and drug discovery scientists
  • Contribute to a shared codebase alongside scientists and engineers, following good software and data-engineering practices

We are looking for candidates who are eager to learn, can work independently, and have a strong interest in applying computational methods to healthcare challenges.

Required qualifications:

  • Currently pursuing or recently completed a degree in Bioinformatics, Computational Biology, Systems Biology, Data Science, AI/ML or a related field
  • Good Python skills and experience working with biological data
  • Solid understanding of core biological concepts (e.g. genes, pathways, phenotypes)
  • Familiarity with basic statistical or machine-learning concepts
  • Clear communication skills for multidisciplinary collaboration 
  • Ability to work in a collaborative environment


Nice to have:

  • Background or strong interest in drug discovery or toxicology
  • Experience in one or more of the following areas: 
    protein-protein interaction data, cheminformatics, protein informatics, human genetics or genomics, biological mechanism data, clinical or toxicology data
  • Experience applying machine learning methods and/or using machine learning frameworks (e.g. PyTorch, TensorFlow)


What we offer:

  • Competitive internship compensation
  • Hybrid working (home + office in Aldgate East)
  • Mentorship from experienced scientists and engineers
  • Opportunity to work on meaningful healthcare challenges
  • Regular feedback and learning opportunities
  • Potential for future full-time employment


Duration: 3-6 months (flexible)


Note: While we appreciate the interest, we are not seeking assistance from recruitment agencies for this position at this time.



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