# Senior Machine Learning Scientist

**Company**: Quotient Therapeutics
**Location**: Cambridge
**Work arrangement**: hybrid
**Experience**: senior
**Job type**: full-time
**Category**: Data Science
**Industry**: Healthcare

**Apply**: https://job-boards.greenhouse.io/flagshippioneeringinc/jobs/8805456002?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_b5e8aa4b-2ca

## Description

Quotient Therapeutics is seeking a Senior Machine Learning Scientist to build models and AI agents that turn somatic mutation data into evidence for target discovery. You will build models of variant effects on protein function and connect them to genomic, single-cell, perturbation, and phenotype data. You will also develop agentic AI workflows that help scientists analyze data, test hypotheses, and make decisions.

Key Responsibilities:

- Build and evaluate models that predict how coding and non-coding variants affect protein function, stability, interactions, and pathway activity.

- Use variant-effect predictions to interpret somatic selection signals.

- 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.

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.

- 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.

- 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.

- Proven ability to work in cross-functional teams and to explain technical results to non-specialists.

## Skills

### Required
- Python
- deep learning
- machine learning
- computational biology
- genomics
- bioinformatics
- statistics
- engineering

### Nice to have
- biomedical foundation models
- AI agents
- Perturb-seq
- single-cell genomics
- genotype–phenotype modelling
- causal inference
- perturbation modelling
- large-scale biological datasets

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