Senior Machine Learning Scientist I/II, Model-Driven Optimization

The role

You will build machine learning methods, data strategies, and closed-loop systems for protein optimization. This role involves developing property models, multi-objective optimization strategies, and LLM-enabled workflows to accelerate therapeutic discovery.

What you will do

• Develop new machine learning methods and systems for lab-in-the-loop protein optimization
• Shape data-generation and data-use strategies for experimental campaigns
• Build and apply LLM-enabled and agentic workflows
• Design, implement, test, and maintain production-quality ML models and data workflows
• Partner with ML engineering and software teams to integrate components into scalable platforms
• Collaborate with protein designers and wet-lab scientists to ground models in experimental reality
• Identify technical gaps, develop proposals, and define milestones
• Communicate across disciplines to raise technical standards

Required qualifications

• PhD in machine learning, computational biology, computer science, applied mathematics, engineering, or a related quantitative field
• Practical experience with probabilistic machine learning, Bayesian optimization, active learning, or related approaches
• Experience developing machine learning methods or systems for biological, biomedical, or experimental scientific data
• Ability to translate ML ideas into systems, tools, or workflows
• Strong Python skills
• Experience with modern ML frameworks such as PyTorch, JAX, or similar tools
• Strong systems thinking and ability to design technical interfaces
• Excellent communication skills

Nice to have

• Experience in protein design, protein engineering, antibody engineering, biologics discovery, or drug development
• Experience partnering with experimental teams on design-build-test-learn cycles, high-throughput screening, directed evolution, pooled libraries, or model-guided experimental campaigns
• Experience with multi-objective optimization, uncertainty calibration, model-guided library design, or experimental campaign planning
• Experience developing and applying deep learning models, including transformer-based architectures
• Experience building or applying LLM agents, scientific copilots, or agentic systems in technical workflows
• Experience contributing to shared ML platforms, libraries, APIs, or developer tooling

Benefits

• Annual bonus
• Equity compensation
• Competitive benefits package

About the company

Generate:Biomedicines is a clinical-stage generative biology company pioneering the AI revolution in drug design and development. The company integrates machine learning with large-scale experimentation to design proteins with defined biological intent.