Director, AI & Machine Learning

The leader will provide technical leadership and hands-on engineering expertise to advance scalable AI and machine learning capabilities across Amgen Research. This role will guide the design, integration, and evolution of AI-enabled solutions, helping translate scientific and business needs into reliable, scalable, and maintainable technical capabilities.

Roles & Responsibilities

• Set AI and ML strategy and roadmap. Develop and guide multi-department AI/ML strategy aligned to R&D, clinical, medical, operations, and commercial priorities.
• Shape and lead the R&D AI/ML roadmap, with a focus on shared scientific data and models, reusable AI capabilities, and scalable end-to-end research workflows.
• Identify investment opportunities with clear scientific, operational, and business value.
• Lead advanced ML programs. Sponsor and direct the design, validation, and scale-up of ML, generative AI, foundation-model, and agentic AI solutions from opportunity framing through production adoption.
• Establish production ML architecture. Drive platform choices, modernization roadmaps, reusable engineering patterns, and quality standards that enable dependable model delivery at scale across multiple departments.
• Establish scalable architecture patterns that integrate scientific data, tools, predictive and generative models, and AI agents into reusable end-to-end scientific workflows.
• Guide MLOps and data platform strategy. Prioritize investments in data quality, lineage, experimentation, evaluation, monitoring, model registry, release governance, and lifecycle operations to reduce delivery time and improve compliance readiness.
• Champion responsible AI and model governance. Ensure AI/ML governance standards are implemented across the portfolio; balance innovation, patient impact, regulatory expectations, security, privacy, and operational risk through practical controls and review forums.
• Build high-performing teams and capability. Recruit, develop, and retain strong ML engineering talent; guide senior professionals and create an inclusive culture of technical excellence, scientific rigor, continuous learning, and accountable execution.
• Drive portfolio value and measurable outcomes. Set investment logic, success measures, and portfolio-level KPIs that demonstrate scientific, operational, and business impact.
• Measure the adoption and value of scientific AI capabilities through outcomes such as workflow completion time, research productivity, result quality, user satisfaction, and production adoption, while managing value, risk, feasibility, adoption, cost, and speed trade-offs.
• Build strategic alliances and influence decisions. Partner with scientists, digital and technology leaders, data/platform teams, quality, legal, compliance, privacy, and information security to plan major changes and integrate AI/ML into real workflows.
• Communicate with executive clarity. Develop clear narratives, roadmaps, and recommendations that enable senior stakeholders to understand technical options, assumptions, risks, and investment decisions.

Basic Qualifications

• Doctorate degree and 4 years of Director, AI & Machine Learning experience OR Master's degree and 8 years of Director, AI & Machine Learning experience OR Bachelor's degree and 10 years of Director, AI & Machine Learning experience
• In addition to meeting at least one of the above requirements, you must have at least 4 years experience directly managing people and/or leadership experience leading teams, projects, programs, or directing the allocation or resources.

Additional Qualifications

• AI/ML technical leadership. Expert AI/ML engineering knowledge with a demonstrated record of setting technical and modeling strategy for scientific research applications.
• Proven ability to evaluate emerging AI/ML methods, make consequential architecture and technology decisions, and translate scientific needs into reusable, scalable AI capabilities and production-grade research workflows.
• Production systems and architecture. Deep hands-on understanding of software engineering and production AI/ML system design, including scalable APIs and pipelines, cloud platforms, model serving, evaluation, observability, and maintainable system architecture.
• Data, MLOps, and lifecycle operations. Experience directing reusable data and MLOps capabilities, including lineage, reproducibility, validation, monitoring, drift detection, CI/CD, incident response, auditability, and model retirement.
• Responsible AI in a regulated environment. Ability to implement fit-for-purpose governance, validation evidence, model documentation, risk controls, access safeguards, and review mechanisms for sensitive or regulated AI/ML applications.
• People and organizational leadership. Demonstrated success building and leading high-performing technical teams, developing senior professionals, informing resource allocation, and guiding advanced programs through major change.
• Strategic stakeholder management. Strong record of building alliances across scientific, business, and technology functions; partnering with scientists and research workflow owners to translate complex scientific needs into scalable AI/ML solutions; influencing decisions without formal authority; and communicating complex ML strategy to leadership audiences.
• Generative AI and systems thinking. Experience setting strategy for foundation models and enterprise AI adoption, balancing innovation, compliance, platform reuse, operating risk, workforce enablement, and scientific or business value.
• Education. Doctorate degree with 10-12+ years of relevant experience in computer science, machine learning, artificial intelligence, computational science, data science, engineering, or a related field; OR Bachelor's degree with 12-14+ years of relevant experience.
• Technical depth. Demonstrated experience with Python and modern ML/deep-learning frameworks (for example, PyTorch, TensorFlow, JAX), along with cloud and data platforms used to deploy AI/ML solutions at scale.
• Certifications. Relevant cloud, AI/ML, or MLOps certifications (for example, AWS, Azure, Google Cloud, Databricks) are a plus.

Preferred Qualifications

• Agentic AI architectures. Experience designing, evaluating, and governing agentic AI architectures, including tool use, orchestration, planning, memory, guardrails, human oversight, and production monitoring.
• Scientific background. Academic or professional background in chemistry, biology, biochemistry, biophysics, computational biology, bioinformatics, or a related life-science discipline.
• Scientific AI and foundation models. Experience with AI/ML enabled approaches for biological and molecular research, including protein structure prediction, Protein Language Models (PLMs), biological foundation models, molecular design, or related computational biology and chemistry applications is a plus.
• Biopharma and regulated domains. Experience applying AI/ML in pharma, biotech, healthcare, or another highly regulated industry, including an understanding of privacy, consent, data provenance, quality, and compliance expectations.
• Enterprise platform experience. Experience partnering with cloud, data engineering, security, and platform teams to build reusable AI/ML capabilities using Azure, Databricks, Snowflake, or comparable technology ecosystems.
• Scientific and executive communication. Ability to turn technical evidence into concise, decision-ready narratives for scientific leaders, business leaders, and senior executives.