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The AI & Data for Engineered Biologics (AIDE) organization at Amgen is seeking a Senior Director to lead our BioIntelligence team. The Senior Director will lead the development and deployment of AI-driven predictive modeling capabilities for biologics discovery in our Large Molecule Discovery organization. The BioIntelligence team applies machine learning, statistical modeling, and generative AI approaches to predict critical properties of engineered biologics and enable data-driven therapeutic design. These capabilities support programs across discovery, protein engineering, immunology, and developability by transforming experimental data into actionable predictive models and scientific software. In this vital role, you will lead a sophisticated multidisciplinary team of machine learning and data scientists responsible for developing scalable AI solutions that accelerate biologics discovery. You will define the scientific and technical strategy for AI-driven biologics property prediction while partnering closely with experimental teams, data engineering groups, and software platform teams across Amgen. This role requires a leader with deep expertise in machine learning for biological systems, experience building and mentoring high-performing scientific teams, and a track record of translating advanced computational methods into impactful research capabilities.
Job Responsibility:
Lead the BioIntelligence 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 BioIntelligence capabilities with broader Research and Large Molecule Discovery priorities
Oversee development of predictive models for key biologics properties, including developability, 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
Requirements:
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
OR Master’s 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
OR Bachelor’s 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 experience directly managing people and/or leadership experience leading teams, projects, programs, or directing the allocation or resources
Nice to have:
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 protein sequence or structure modeling, antibody engineering, or computational immunology
Strong leadership experience managing multidisciplinary computational science teams
Track record of publications, patents, or deployed technologies in AI for life sciences
What we offer:
A comprehensive employee benefits package, including a Retirement and Savings Plan with generous company contributions, group medical, dental and vision coverage, life and disability insurance, and flexible spending accounts
A discretionary annual bonus program, or for field sales representatives, a sales-based incentive plan