AI Scientist

Senior Machine Learning Scientist – AI Computational Biology (Remote, UK)

Location: Remote (UK-based, London time zone)

Type: Full-time

Focus: Genomics, single-cell biology, production ML systems ( RNA-Seq AND WGS)

The Opportunity

We’re building production-grade computational methods that turn complex biological data into robust, interpretable biomarkers used daily to advance cancer research and drug development.

This is not a purely exploratory research role. You’ll take true end-to-end ownership of methods that sit between raw omics data and biological interpretation — designing them, stress-testing them, and running them reliably in production.

If you enjoy combining deep machine learning , real biological signal , and strong engineering practices , this role is built for you.

What You’ll Do

Own production biomarker methods

• Design and implement genomics and transcriptomics pipelines (RNA-seq, single-cell, WGS/WES).
• Turn complex molecular data into scalable, reproducible biomarkers with clear assumptions and limitations.
• Continuously improve methods based on biological insight, feedback, and observed failure modes.

Apply ML & AI to biological interpretation

• Develop and fine-tune deep learning models for biological representation learning (e.g. single-cell, multimodal data).
• Prototype AI-driven approaches (including LLMs and agentic workflows) for hypothesis generation and interpretation.
• Decide where ML meaningfully adds value — and where simpler methods are better.

Evaluate emerging methods

• Track new approaches from literature and open source.
• Implement, benchmark, and critically assess robustness and generalisability.
• Drive adoption decisions based on evidence, not novelty.

What We’re Looking For

Background

• MSc / PhD (or equivalent industry experience) in Machine Learning, Computer Science, Computational Biology, Bioinformatics, or related field.
• Strong interest in biology and translational research; oncology exposure is a plus.

Technical profile

• Strong Python skills with experience building complex ML or data-processing pipelines.
• Hands-on experience with omics data (single-cell RNA-seq, bulk RNA-seq, WGS/WES, or multimodal genomics).
• Deep learning experience (e.g. transformers, VAEs, contrastive learning, GNNs).
• Familiarity with production-quality practices:
• Version control (Git)
• Reproducibility & testing
• Containerisation (Docker) and/or CI/CD

Mindset

• Enjoys owning methods long-term, not just publishing or prototyping.
• Comfortable working across biology, ML, and engineering.
• Able to clearly explain trade-offs to both technical and non-technical stakeholders.