Job Description

Generate Biomedicines seeks a machine learning scientist to develop methods and systems for lab-in-the-loop protein optimization. The role bridges machine learning, protein design, and experimental science to advance therapeutic discovery through sequential decision-making and data-driven design strategies.

Responsibilities

• Develop machine learning methods and systems for protein optimization including property models and multi-objective strategies
• Design data-generation and data-use strategies to maximize experimental campaign informativeness
• Build and apply LLM-enabled workflows for design hypothesis exploration and iterative learning
• Implement production-quality ML models, software components, and data workflows with focus on reliability and reproducibility
• Collaborate with protein designers and experimentalists to ground models in experimental reality and drive impact

Requirements

• PhD in machine learning, computational biology, computer science, applied mathematics, or related quantitative field
• Expertise in probabilistic machine learning, Bayesian optimization, active learning, or experimental design
• Experience building ML methods for biological or scientific experimental data
• Demonstrated ability to translate ML ideas into systems affecting real scientific decisions
• Strong Python programming skills and software engineering discipline
• Ability to collaborate across protein design, wet-lab science, and engineering teams

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