Senior Machine Learning Scientist

Quotient Therapeutics · Cambridge, UK

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Role summary

Quotient Therapeutics seeks a Machine Learning Scientist to build models and AI agents that interpret somatic mutation data for drug target discovery. The role combines variant-effect prediction, foundation model development, and agentic AI workflows to connect genomic data with protein function and disease biology.

Where this listing comes from

This listing was compiled by RoleLantern from the employer's publicly available posting on Greenhouse, captured and rephrased by RoleLantern using automated (AI) tools on 2026-10-06 (originally posted 2026-10-06). It was not submitted or confirmed by the employer through RoleLantern. Details may be inaccurate, incomplete, outdated, or no longer available; please confirm with the employer's official posting before applying.

What they're looking for

PhD in machine learning, computer science, computational biology, genomics, or related field; MSc with equivalent experience acceptable
Strong Python proficiency and practical deep learning systems experience (PyTorch, JAX)
Experience training and evaluating ML models on complex biological datasets
Knowledge of variant effects on protein structure and function; familiarity with variant-effect prediction tools
Active use of AI coding assistants, LLM-based workflows, or agentic development tools
Depth in biomedical foundation models, AI agents, single-cell genomics, or causal inference

What you'll be doing

Build and evaluate models predicting how coding and non-coding variants affect protein function using protein language models and structure-based methods
Interpret somatic selection signals and classify mutations as loss-of-function, gain-of-function, or neutral
Design and build AI agents and agentic workflows to retrieve evidence, run analyses, and support hypothesis testing
Train, fine-tune or adapt foundation models and representation-learning methods for target discovery
Communicate results clearly to cross-functional teams, including model assumptions, limitations and next steps

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Date posted1 day ago

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