Senior Machine Learning Scientist

About This Opportunity

We are representing a confidential biotechnology organization in Cambridge that is building advanced AI and machine learning systems to transform complex biological data into actionable scientific insights. This Senior Machine Learning Scientist role sits at the intersection of ML, genomics, causal biology, and therapeutic target discovery.

You will build models, design agentic AI workflows, create systems that automate analysis and reasoning, and develop AI-assisted tools that connect genotype, cell state, perturbation, phenotype, and disease mechanisms. This is a hands-on position for someone who writes clear, reliable code, leverages modern AI development tools, and wants their work to influence real biological and therapeutic decisions.

Responsibilities

• Build machine learning 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 maintaining code reliability, readability, testing, and reproducibility
• 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 with computational scientists, experimental biologists, software engineers, and target discovery teams
• Communicate results clearly, including model assumptions, limitations, and recommended next steps

What We're Looking For

Strong candidates may come from academia, industry, open-source contributions, or hands-on research backgrounds. You do not need to meet every qualification to apply.

• PhD, Master's degree, or equivalent experience in machine learning, computer science, computational biology, genomics, bioinformatics, statistics, engineering, or 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 areas: 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 publications, thesis work, open-source projects, industry projects, or substantial applied research
• Collaborative mindset with commitment to respectful disagreement and continuous learning
• Ability to seek diverse perspectives and practice active listening
• Team-first approach to decision-making and calculated risk-taking

How We Work With You

Candidates apply directly through Get A Job.ai. Our talent team will conduct an initial screening to understand your background and match you with the right opportunity. We then submit qualified candidates to our client for consideration. Please do not contact the client directly, as all communication is managed through our recruiting process to ensure the best experience for everyone involved.

Pay

Compensation details will be discussed during the screening process based on experience and qualifications.

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