Senior ML Scientist – Somatic Genomics & Target Discovery

Pioneering Medicines; United Kingdom; Hybrid; GBP 70,000 - 100,000; Full time

Job summary

Quotient Therapeutics seeks a (Senior) Scientist, Machine Learning to turn somatic mutation data into actionable evidence for target discovery. You will build models of variant effects on protein function and connect them to multi-omics data, while developing AI workflows to help scientists test hypotheses and make decisions.

This hands-on role sits on a cross-functional team. You will write clear, tested code, use modern AI tools, and collaborate with computational scientists, experimental

Qualifications

• PhD in machine learning, computer science, computational biology, genomics, bioinformatics, statistics, engineering or a related field, or an MSc with equivalent experience.
• Strong Python skills and experience building practical deep learning systems (e.g. PyTorch, JAX).
• Experience training, evaluating or applying ML models to complex real-world biological data.
• Understanding of how genetic variants affect protein structure and function, and experience with variant-effect prediction methods (e.g. protein language models such as ESM, structure-based models, or tools such as AlphaMissense).
• Regular use of AI coding assistants, LLM-based workflows or agentic development tools in your own work.
• Depth in one or more of the following: biomedical foundation models, AI agents, Perturb-seq or single-cell genomics, genotype-phenotype modelling, causal inference, perturbation modelling, or large-scale biological datasets.

Responsibilities

• Build and evaluate models that predict how coding and non-coding variants affect protein function, stability, interactions and pathway activity, using protein language models, structure-based methods and related approaches.
• Use variant-effect predictions to interpret somatic selection signals, including whether mutations are likely loss-of-function, gain-of-function or neutral, and where they cluster in protein structure.
• Design, build and evaluate AI agents and agentic workflows that retrieve evidence, run analyses and support hypothesis testing and target assessment.
• Train, fine-tune or adapt foundation models, language models and representation-learning methods for target discovery.
• Use AI coding tools to speed up development while keeping code readable, tested and reproducible.
• Work as a core member of cross-functional project teams, share ownership of team goals, and contribute to shared codebases, reviews and tools.
• Communicate results clearly to scientists and target discovery leadership, including model assumptions, limitations and next steps.

Skills

Python

Deep learning

PyTorch

JAX

Education

PhD in ML or related field

Tools

PyTorch

JAX

LLM tooling

Job description

Quotient Therapeutics seeks a (Senior) Scientist, Machine Learning to turn somatic mutation data into actionable evidence for target discovery. You will build models of variant effects on protein function and connect them to multi-omics data, while developing AI workflows to help scientists test hypotheses and make decisions.

This hands-on role sits on a cross-functional team. You will write clear, tested code, use modern AI tools, and collaborate with computational scientists, experimental