Senior Machine Learning Scientist in Cambridge

We are looking for a Machine Learning Scientist to build AI and machine learning systems that turn complex biological data into actionable scientific insight. You will work at the intersection of ML, genomics, causal biology, and target discovery. The role includes building models, agentic AI workflows, systems that help automate analysis, reasoning, and decision support, and AI assisted tools that help connect genotype, cell state, perturbation, phenotype, and disease mechanism. This is a hands‑on role for someone who enjoys writing clear, reliable code, uses modern AI tools to move faster, and wants their work to shape real biological and therapeutic decisions.

Responsibilities:

• Build ML models for genomic, single-cell, perturbation, and phenotype data.
• Develop agentic AI workflows that help scientists analyze data, test hypotheses, and make better decisions.
• Use AI coding tools to accelerate development while keeping code reliable, readable, tested, and reproducible.
• Train and fine tune foundation models, language models, and representation learning methods for target discovery.
• Model relationships between genotype, cell state, perturbation response, causal biology, and disease.
• Collaborate closely with computational scientists, experimental biologists, software engineers, and target discovery teams.
• Communicate results clearly, including model assumptions, limitations, and next steps.

Qualifications:

• You do not need to meet every item to apply. Strong candidates may come from academia, industry, open-source work, or hands‑on research.
• PhD, Master’s degree, or equivalent experience in machine learning, computer science, computational biology, genomics, bioinformatics, statistics, engineering, or a related field.
• Strong Python coding skills and experience building practical Deep Learning ML systems.
• Experience using AI coding tools, coding assistants, LLM-based workflows, or agentic development tools.
• Experience training, evaluating, or applying ML models to complex real-world data.
• Depth in one or more of: biomedical foundation models, agentic AI, Perturb-seq, single-cell genomics, genotype-phenotype modeling, causal inference, perturbation modeling, or large-scale biological datasets.
• Evidence of technical depth through papers, thesis work, open-source projects, industry projects, or substantial hands‑on applied research.

Values and Behaviors:

• Encourage respectful disagreement and cultivate open‑minded, ego‑free interactions to continuously push each other towards excellence.
• Seek out diverse perspectives; practice active listening and genuine curiosity to ensure all contributions are valued, regardless of source.
• Recognize the impact of your behavior, language and attitudes, and strive for balanced, meaningful exchanges that enhance mutual growth and understanding in all interactions.
• Use a company‑first mindset to guide decision‑making; prioritize team over individual success.
• Take calculated risks and challenge convention in the quest for exceptional outcomes.

About Quotient:

Quotient Therapeutics is a privately‑held, early stage company developing breakthrough medicines informed by natural somatic genetic diversity present in patients. Through our work in somatic genomics, we are forging a new status quo for biopharma research and development across a broad pipeline of internal and partnered programs.