Senior ML Scientist - Single-Cell Biology & Genomics

Relation Therapeutics; Greater London; On-site; GBP 90,000 - 140,000; Full time

Job summary

Relation Therapeutics in London seeks a Senior Machine Learning Scientist who combines ML fundamentals with deep biological data understanding. You will develop ML approaches for single-cell and multiomic datasets to uncover cellular responses and inform therapeutic strategy.

You'll work at the intersection of ML and biology, collaborating with wet-lab teams, translating questions into modelling problems, and communicating findings across disciplines in a matrixed environment.

Qualifications

• PhD in ML, computational biology, statistics or related field.
• Hands-on experience building ML models for biological data (single-cell, multiomics, perturbational datasets).
• Strong foundations in modern ML (probabilistic modelling, representation learning, geometric deep learning).
• Fluency in Python and ML frameworks (PyTorch, JAX) with experience at scale.

Responsibilities

• Design ML models for single-cell and multiomic data with attention to biological structure.
• Develop representation learning, probabilistic, or structured prediction methods for cellular state and perturbation response.
• Shape the team's research roadmap on cellular modelling.
• Collaborate with experimental teams to translate biological questions into modelling problems and interpret outputs.

Skills

Python

ML frameworks

Biological data

Communication

Education

PhD in machine learning, computational biology, statistics

Tools

PyTorch

JAX

Job description

Relation Therapeutics in London seeks a Senior Machine Learning Scientist who combines ML fundamentals with deep biological data understanding. You will develop ML approaches for single-cell and multiomic datasets to uncover cellular responses and inform therapeutic strategy.

You'll work at the intersection of ML and biology, collaborating with wet-lab teams, translating questions into modelling problems, and communicating findings across disciplines in a matrixed environment.