Senior ML Scientist, Generative Biomedicine

relationrx

Greater London

On-site

GBP 90,000 - 130,000

Full time

14 days+

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Job summary

Relation is a TechBio company advancing medicines using single-cell multi-omics and machine learning in London. We seek a Machine Learning Scientist who blends ML fundamentals with deep biological data understanding to build generative and predictive models of cellular behaviour.

You will join a team with access to multi-omic data, advanced compute, and interdisciplinary expertise. Expect to push the boundaries of generative modelling in real-world systems.

Qualifications

  • PhD in machine learning, computer science, statistics, or a related quantitative field.
  • Strong ML foundations with depth in modelling high-dimensional data.
  • Excellence in Python and scalable ML tooling.
  • Experience training models at scale and moving from idea to production.
  • Comfort with messy real-world scientific data.

Responsibilities

  • Design and implement generative modelling approaches learning intervention effects from diverse biological data.
  • Develop models focusing on generalisation, counterfactuals, and experimental design.
  • Collaborate with experimental teams to validate computational hypotheses through iterative strategies.
  • Evaluate models for causal coherence, mechanistic fidelity, and real-world utility.
  • Communicate findings clearly to multi-disciplinary colleagues.

Skills

Python programming
ML tooling
Distributed training
Model deployment

Education

PhD in ML/CS/Statistics or related

Tools

Python
HPC

Job description

Relation is a TechBio company advancing medicines using single-cell multi-omics and machine learning in London. We seek a Machine Learning Scientist who blends ML fundamentals with deep biological data understanding to build generative and predictive models of cellular behaviour.

You will join a team with access to multi-omic data, advanced compute, and interdisciplinary expertise. Expect to push the boundaries of generative modelling in real-world systems.

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