Director, AI & Machine Learning

Career Category Information Systems Job Description Director, AI & Machine Learning ABOUT AMGEN At Amgen, if you feel like you are part of something bigger, it is because you are. Our shared mission - to serve patients living with serious illnesses - drives all that we do. Since 1980, we have helped pioneer the world of biotech in our fight against the world's toughest diseases. With a focus on oncology, inflammation, general medicine, and rare disease, we reach millions of patients each year. As a member of the Amgen team, you will help make a lasting impact on the lives of patients as we research, manufacture, and deliver innovative medicines. Our culture is collaborative, innovative, and science based. Join us and transform the lives of patients while transforming your career. ABOUT THE ROLE Role Description Let's do this. Let's change the world. The Director, AI & Machine Learning will play a key role in advancing Amgen Research’s AI capabilities by providing technical direction and engineering leadership across strategic initiatives. The role will help accelerate the development and adoption of AI solutions while building a strong, reusable technical foundation that can support evolving priorities across the Research organization. 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. The successful candidate combines strong AI/ML and software engineering expertise with technical leadership, product thinking, and scientific curiosity. You will partner across multidisciplinary teams to guide technical decisions, establish engineering best practices, evaluate emerging technologies, and enable the effective adoption of AI capabilities across a broad portfolio of Research initiatives. 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. What we expect of you We are all different, yet we all use our unique contributions to serve patients. The professional we seek is a Director with these qualifications. 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. Your managerial experience may run concurrently with the required technical experience referenced above 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 that support reusable scientific capabilities and workflows.. Data, MLOps, and lifecycle operations. Experience directing reusable data and MLOps cap