Senior Machine Learning Scientist

Description

This is a Scientist or Senior Scientist, Machine Learning role, with the level set by experience.

The company is privately held, early stage and finds drug targets through somatic genomics.

It sequences tissue at high accuracy to detect rare somatic mutations in cells from diseased, non-cancerous human tissue. Mutations that help cells survive or grow in disease point to causal biology.

That evidence shows whether to copy protective mutations or block disease-driving ones.

The founder, the company, has created more than 100 companies over the past twenty years.

Responsibilities

• Build models and AI agents that turn somatic mutation data into evidence for target discovery.
• Find out what a mutation does to a protein, and whether that effect explains why cells carrying it are selected in disease.
• Model how variants change protein function and connect those models to genomic, single-cell, perturbation and phenotype data.
• Create agentic AI workflows that help scientists analyse data, test hypotheses and make decisions.
• Build and assess models that predict how coding and non-coding variants affect function, stability, interactions and pathway activity, using protein language models, structure-based methods and similar approaches.
• Use those predictions to read somatic selection signals, judging whether mutations are loss-of-function, gain-of-function or neutral, and where they cluster in protein structure.
• Design, build and test AI agents that gather 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 move faster 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 code, reviews and tools.
• Explain results to scientists and target discovery leaders, including assumptions, limitations and next steps.
• This is hands-on work: you write clear, tested code and collaborate daily with computational scientists, experimental biologists, engineers and target discovery leads who act on your results.

Requirements

• A PhD in machine learning, computer science, computational biology, genomics, bioinformatics, statistics, engineering or a related field, or an MSc with equivalent experience.
• Strong Python and hands-on experience building deep learning systems, for example with PyTorch or JAX.
• Experience training, evaluating or applying ML models to complex real-world biological data.
• An understanding of how genetic variants alter protein structure and function, plus experience with variant-effect methods such as ESM, structure-based models or AlphaMissense.
• Routine use of AI coding assistants, LLM-based workflows or agentic development tools in your own work.
• Depth in at least one of: biomedical foundation models, AI agents, Perturb-seq or single-cell genomics, genotype-phenotype modelling, causal inference, perturbation modelling, or large-scale biological datasets.
• A record of working in cross-functional teams and explaining technical results to non-specialists.
• Evidence of technical depth, such as papers, thesis work, open-source or industry projects, or substantial applied research.
• Encourage respectful disagreement and an open, ego-free approach that drives the team toward excellence.
• Seek diverse views, listen actively and value every contribution, whatever its source.
• Be mindful of how your words and attitudes land, and aim for balanced exchanges that help everyone grow.
• Put the company first in decisions, favouring team success over personal success.
• Take calculated risks and question convention in pursuit of exceptional outcomes.

Conditions

Based in Cambridge, UK, in a hybrid model with a minimum of 3 days per week in the office.

Hiring is open to all, and the company and the company do not discriminate on race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.