Apply, adapt, and in some cases create multi-modal foundation models such as large language models (LL - Ms), diffusion models, and encoder architectures to answer biological domain-specific questions. Address real-world biological modeling challenges such as data sparsity, class imbalance, noise, experimental bias, and heterogeneity of effects. Thoughtful model evaluation that incorporates appropriate benchmarks, statistical tests, and problem understanding to support technical and business decisions. Work in close collaboration with partners across the organization including wet-lab scientists, Research IT, and other computational scientists to broaden the impact of AI developments. Maintain and share up-to-date knowledge of modern advances in the field, including presenting work at public conferences Basic Qualifications. Bachelor's Degree 7 years of academic / industry experience. Or Master's Degree 5 years of academic / industry experience. Or PhD 2 years of academic / industry experience. Preferred Qualifications? A PhD. with 0 years industry research experience or an M. S. with 3 years industry research experience in computer science, statistics, computational biology, or another quantitative field. Expert-level understanding of and experience using deep learning tools and approaches (transformer-based encoders/decoders, LL - Ms, reinforcement learning, etc.) as demonstrated through publications or projects. Hands-on experience leading the building and scaling of deep learning training pipelines on multi-GPU computational infrastructure using Py. Torch, Huggingface, and/or other tools. Knowledge of or the ability to learn biological concepts and data types, including the ability to work and communicate effectively with biologists. Excellent verbal and written communication skills. Fluent verbal and written English language skills prerequisite. Knowledge of or the ability to learn biological concepts and data types, including the ability to work and communicate effectively with biologists. Prior experience in pharmaceutical application areas is a plus.