Director, AI and Machine Learning

Career Category

Scientific

Job Description

Join Amgen’s Mission of Serving Patients

At Amgen, if you feel like you’re part of something bigger, it’s because you are. Our shared mission—to serve patients living with serious illnesses—drives all that we do.

Since 1980, we’ve helped pioneer the world of biotech in our fight against the world’s toughest diseases. Amgen is advancing a broad and deep pipeline and portfolio of medicines to treat heart disease, obesity and obesity-related conditions, rare diseases, inflammatory conditions and cancer.  As a member of the Amgen team, you’ll help make a lasting impact on the lives of patients as we research, manufacture, and deliver innovative medicines to help people live longer, fuller happier lives.

Our award-winning culture is collaborative, innovative, and science based. If you have a passion for challenges and the opportunities that lay within them, you’ll thrive as part of the Amgen team. Join us and transform the lives of patients while transforming your career.

Director, AI and Machine Learning

What you will do

Let’s do this. Let’s change the world. In this vital role you will own an asset-agnostic portfolio that turns human multi-omics, imaging, biomarker, and clinical data into insights supporting clinical drug development and regulatory evidence generation.

You will connect scientific strategy with technical execution by advancing predictive analytics, translation and reverse translation, enabling cross-study learning, and building the infrastructure and ways of working needed for effective adoption of generative and agentic AI. The role combines scientific judgment, technical depth, and people leadership.

Key Responsibilities:

Set the strategy for shared AI and machine learning capabilities

• Define and execute the group's AI/ML strategy across therapeutic areas, prioritizing reusable capabilities that address clinical development questions and regulatory evidence needs. Translate scientific priorities into a coordinated roadmap for methods, platforms, and analytical products.

• Partner with therapeutic area computational biology leaders and program-facing scientists to integrate shared capabilities into development workflows. Balance innovation, delivery, adoption, resource needs, and operating costs.

• Influence Precision Medicine AI/ML strategy

Own the scientific analytics stack and infrastructure roadmap

• Lead the architecture and evolution of the group's analytics stack, spanning data preparation and integration, computational workflows, model development, evaluation, deployment, and scientific interpretation in clinical development settings.

• Partner with enterprise technology, data engineering, security, and infrastructure teams to deliver scalable AWS-based computing and AI capabilities aligned with enterprise architecture and governance.

• Establish reusable software, workflows, and standards for reproducibility, version control, testing, documentation, data lineage, and ongoing platform and model operations.

Advance clinical and translational analytics

• Lead the development and application of statistical, machine learning, deep learning, and other AI methods to integrate multi-omics, imaging, biomarker, and clinical data from human studies.

• Develop reusable approaches for patient stratification, response prediction, pharmacodynamic analysis, and mechanism-of-action studies, linking biomarker and imaging findings to clinical endpoints, efficacy, and safety.

• Direct cross-study and cross-asset analyses that support translation and reverse translation, using biological hypotheses to inform clinical biomarker strategies and clinical observations to generate mechanistic hypotheses for experimental testing and future studies.

• Anchor analytical strategies in clinical trial design, endpoints, patient populations, assay limitations, confounding, missing data, and uncertainty.

• Apply validation appropriate to the scientific question, clinical decision, and intended evidentiary use.

Guide human data strategy and evidence gap analysis

• Identify and prioritize evidence gaps across clinical programs and therapeutic areas, and determine where additional human datasets could strengthen biomarker interpretation, predictive models, and clinical or regulatory evidence generation.

• Lead scientific and analytical evaluation of external human datasets, recommending which to acquire, license, and internalize based on relevance to priority questions, population and outcome coverage, data quality, reuse potential, permitted uses, and integration requirements.

Agentify existing workflows and operationalize AI

• Lead agentification of existing analytical workflows, redesigning repeatable steps with AI assistants and coordinated agents to increase operational productivity and reduce routine manual quality-control effort.

• Automate routine validation, consistency checks, and QC triage, directing exceptions and scientific interpretation to appropriate experts with traceable review decisions.

• Guide the use of Codex and other enterprise-approved AI tools to develop and orchestrate agent-enabled workflows connecting scientific data, analytical tools, models, and expert review.

• Establish evaluation and monitoring approaches for scientific accuracy, reliability, reproducibility, appropriate tool use, and human oversight.

Support clinical and regulatory evidence generation

• Embed data privacy, access controls, provenance, model documentation, and fit-for-purpose quality practices into shared capabilities used with sensitive human data and clinical analytical workflows.

• Partner with biostatistics, clinical development, precision medicine, regulatory, safety, medical, and quality colleagues to align analysis plans with clinical and regulatory evidence needs and document assumptions, limitations, and interpretation.

• Enable traceable, scientifically reviewed analyses for clinical development decisions, regulatory submissions and health authority questions, publications, and medical and scientific communications, with evidence standards appropriate to each intended use.

Lead people and evolve how the group works

• Manage, coach, and develop a scientific and technical team. Set clear priorities and performance expectations and build an inclusive culture of scientific rigor, collaboration, and accountability.

• Strengthen AI fluency across computational biologists, bioinformaticians, statisticians, mathematicians, data scientists, machine learning scientists, and engineers through practical demonstrations, training, and collaboration.

• Lead adoption of new tools and working practices. Track improvements in analytical turnaround time, manual QC effort, reuse, adoption, scientific quality, and decision value.

• Communicate strategy, results, investment needs, and trade-offs clearly to scientific, technical, and executive audiences.

What we expect of you

We are all different, yet we all use our unique contributions to serve patients. The Director we seek is a leader who combines deep quantitative expertise with an understanding of human biology and clinical development, and who can translate emerging AI capabilities into sustained scientific impact. The successful candidate will bring the following qualifications:

Basic Qualifications:

Doctorate degree and 4 years of relevant experience overall in computational biology, bioinformatics, quantitative science, data science, machine learning, or related fields.

OR

Master’s degree and 8 years of relevant experience overall in computational biology, bioinformatics, quantitative science, data science, machine learning, or related fields.

OR

Bachelor’s degree and 10 years of relevant experience overall in computational biology, bioinformatics, quantitative science, data science, machine learning, or related fields.

In addition to meeting at least one of the above requirements, you must have at least 4 years of 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

Preferred Qualifications:

• Demonstrated AI/ML experience, including development, evaluation, and delivery of computational methods or capabilities that address substantive scientific problems.

• Recognized scientific eminence in AI/ML, computational biology, or translational science, demonstrated by a sustained record of influential peer-reviewed publications and contributions that advance methods or scientific practice.

• Experience analyzing or integrating complex human multimodal data, including multi-omics, imaging, clinical biomarkers, or clinical outcomes, with an understanding of the challenges of high-dimensional biomedical data.

• Experience delivering translational analytics in clinical drug development, with an understanding of clinical trial design, endpoints, biomarker analysis plans, and the documentation and review needed for regulatory evidence generation.

• Practical familiarity with Codex, AI-assisted development, and the development and orchestration of AI agents; familiarity with enterprise generative and agentic AI solutions and their integration into scientific workflows.

• Familiarity with AWS infrastructure, cloud-based analytical environments, and the requirements for scalable scientific computing and AI/ML delivery.

• Ability to lead across functions, influence stakeholders, manage competing priorities, and communicate complex scientific and technical concepts clearly.

• At least 15 years of relevant experience overall in computational biology, bioinformatics, quantitative science, data science, machine learning, or related fields.

• PhD in a relevant computational, quantitative, engineering, or biomedical discipline.

• Demonstrated success taking scientific AI/ML capabilities from research or prototype into routine organizational use.

• Experience developing reusable methods for multimodal integration, predictive modeling, or cross-study learning across more than one therapeutic area.

• Experience with modern software engineering, MLOps, model evaluation, and the operation of enterprise AI systems.

• Experience leading organizational change and helping multidisciplinary teams adopt new analytical technologies and working practices.

What you can expect of us

As we work to develop treatments that take care of others, we also work to care for your professional and personal growth and well-being. From our competitive benefits to our collaborative culture, we’ll support your journey every step of the way.

The expected annual salary range for this role in the U.S. (excluding Puerto Rico) is posted. Actual salary will vary based on several factors including but not limited to, relevant skills, experience, and qualifications.

In addition to the base salary, Amgen offers a Total Rewards Plan, based on eligibility, comprising of health and welfare plans for staff and eligible dependents, financial plans with opportunities to save towards retirement or other goals, work/life balance, and career development opportunities that may include:

• 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

• Stock-based long-term incentives

• Award-winning time-off plans

• Flexible work models where possible. Refer to the Work Location Type in the job posting to see if this applies.

Apply now and make a lasting impact with the Amgen team.

careers.amgen.com

In any materials you submit, you may redact or remove age-identifying information such as age, date of birth, or dates of school attendance or graduation. You will not be penalized for redacting or removing this information.

Application deadline

Amgen does not have an application deadline for this position; we will continue accepting applications until we receive a sufficient number or select a candidate for the position.

Sponsorship

Sponsorship for this role is not guaranteed.

As an organization dedicated to improving the quality of life for people around the world, Amgen fosters an inclusive environment of ethical, committed and highly accomplished people who respect each other and live the Amgen values to continue advancing science to serve patients. Together, we compete in the fight against serious disease.

Amgen is an Equal Opportunity employer. We make all employment decisions without regard to race, color, religion, sex or any other characteristic protected by applicable law.

We provide reasonable accommodations to applicants and employees with disabilities, consistent with legal requirements. Please contact us to request accommodation.

Amgen will consider for employment qualified applicants with criminal histories in a manner consistent with the San Francisco Fair Chance Ordinance.

Salary Range

USD - USD