Design and implement novel scientific approaches for biophysical modeling and foundation model-driven analysis of multi-modal clinical and genomic data for biomarker and target discovery to improve patient selection and enable next-generation assets.
Design, develop, and implement analytical solutions using a variety of commercial and open-source tools (common tools include PyTorch and scikit-learn).
Connect and collaborate with subject matter experts in biology, genomics, and medicine.
Identify opportunities to apply the latest advancements in Machine Learning and Artificial Intelligence to build, test, and validate predictive models.
Develop and embed automated and agentic processes for predictive model validation, deployment, and implementation.
Deploy your algorithms to production to identify actionable insights from large databases.

Master’s degree in computer science, applied math, statistics, physics, systems biology, computational biology, bioinformatics, or related field
Experience in Python programming and knowledge in machine learning, statistics, and applied math.
Familiarity with modern machine learning methods (generative models, representation learning)
Experience in building deep learning models, preferably with exposure to biophysical modeling, functional genomics, molecular and cellular biology or to modeling dynamical systems
Experience with at least one Deep Learning framework such as PyTorch

AI/ML Engineer

Key skills