_{23rd January, 2026}

Member of technical staff
London – Hybrid
  
We are looking for a technical lead for a company focused on building foundation models for drug discovery.

Lead research into advanced machine learning approaches for multi-omics data, including foundation models and self-supervised learning. Build models that learn robust, reusable biological representations across genomics, transcriptomics, proteomics, and clinical data. Apply these methods to functional genomics challenges, supporting target identification from GWAS and causal variant analyses.
  
Responsibilities

- Define and drive the research agenda for foundation models applied to functional genomics
- Design and develop novel architectures for integrating multi-modal biological data, including genomics, transcriptomics, proteomics, and clinical datasets
- Develop self-supervised learning approaches tailored to large-scale omics and clinical data
- Lead research into graph neural networks and transformer-based architectures for modelling biological networks
- Publish and present research at leading conferences, building the organisation’s external scientific profile
  
What we are looking for:

- PhD in Computer Science, Machine Learning, Computational Biology, or related field (Plus several years of industry experience in the field)
- Strong publication record
- Deep expertise in foundation models, self-supervised learning, or representation learning
- Experience with multi-modal learning or data integration
- Proficiency in PyTorch and experience training large-scale models
- Experience working in biological setting (working with genomics/transcriptomics/proteomics/clinical data)
- Strong communication skills, team player, motivated by improving drug discovery.
  

If you are excited about working at the intersection of cutting edge machine learning and high impact biology this could be an exciting opportunity for you. Apply today for a confidential discussion about this position.
  

Apply For Job