Foundation and generative models for biomolecules

Role: Develop and refine cutting-edge machine learning models for biomolecule design to support therapeutic discovery. Responsibilities: implement, train, and optimize generative models using large and diverse datasets; analyze and visualize model outputs; create tools for experimentation and production; collaborate closely with biologists to gather training data; mentor team members. Requirements: 3+ years of hands-on ML model development; proficiency in Python, PyTorch, or similar frameworks; strong software engineering skills; ability to work with distributed training and large-scale models; experience with biosequence data and datasets. Preferred qualifications: PhD in AI/ML, computer science, or related fields; experience with generative models, model parallelism, and bioinformatics; familiarity with molecular biology and drug development. HighValue: focus on biomolecular generative modeling, large-scale foundation models, genomic and protein data, interdisciplinary collaboration with computational and experimental biologists. WorkSetup: primarily in-office with US and Europe team collaboration; travel several times per year.