Senior Director BioIntelligence

Senior Director Bio Intelligence What you will do

In the AI & Data for Engineered Biologics (AIDE) organization at Amgen, we are seeking a Senior Director to lead our Bio Intelligence team. You will develop and deploy AI-driven predictive modeling capabilities for biologics discovery in our Large Molecule Discovery organization, applying machine learning, statistical modeling, and generative AI to predict critical properties of engineered biologics and enable data‑driven therapeutic design.

In this vital role, you will lead a sophisticated multidisciplinary team of machine learning and data scientists, define the scientific and technical strategy for AI-driven biologics property prediction, and partner closely with experimental teams, data engineering groups, and software platform teams across Amgen.

• Lead the Bio Intelligence Team within our Large Molecule Discovery organization, defining strategy and priorities for AI-driven biologics modeling.
• Develop and execute a roadmap for machine learning and AI approaches that accelerate engineered biologics discovery.
• Align Bio Intelligence capabilities with broader Research and Large Molecule Discovery priorities.

• Oversee development of predictive models for key biologics properties, including develop ability, stability, manufacturability, and immunogenicity.
• Advance modeling approaches using modern AI techniques such as protein language models, generative modeling and inverse folding, representation learning, active learning, and Bayesian optimization.
• Guide the use of multimodal biological datasets including sequence, structure, and experimental assay data.

• Lead development of production-quality research software and deployable ML models used across discovery teams.
• Partner with software engineering and data platform teams to ensure models are scalable, reproducible, and integrated into R&D workflows.
• Establish best practices for MLOps, model lifecycle management, and reproducible scientific computing.

• Work closely with teams across protein engineering, immunology, display technologies, systems biology, and discovery platforms.
• Partner with experimental scientists to design data generation strategies and active learning loops that improve model performance.
• Collaborate with data engineering and informatics groups to improve data accessibility, quality, and reuse across the discovery ecosystem.

• Build, mentor, and lead a high-performing team of machine learning scientists and computational biologists.
• Foster a culture of scientific rigor, innovation, and collaboration between computational and experimental scientists.
• Drive adoption of AI solutions across research teams by ensuring models are interpretable, robust, and scientifically trusted.

We are all different, yet we all use our unique contributions to serve patients. The dynamic professional we seek is a leader with these qualifications.

• Doctorate degree in Computational Biology, Machine Learning, Bioinformatics, Computer Science, Biophysics, or related field and 5 years of experience applying machine learning or computational modeling to biological systems.
• Masters degree in Computational Biology, Machine Learning, Bioinformatics, Computer Science, Biophysics, or related field and 9 years of experience applying machine learning or computational modeling to biological systems.
• Bachelors degree in Computational Biology, Machine Learning, Bioinformatics, Computer Science, Biophysics, or related field and 11 years of experience applying machine learning or computational modeling to biological systems.
• At least 5 years of experience directly managing people and/or leading teams, projects, programs, or directing the allocation of resources.

• Experience developing machine learning models for biologics properties.
• Experience with protein language models, diffusion models, generative modeling, or structure-based design.
• Experience deploying ML models into production scientific software platforms.
• Expertise in…