Sr. Scientist – AI/ML Governance & Operations Full-time

Sr. Scientist – AI/ML Governance & Operations (Large Molecule Discovery Informatics)

If you feel like you're part of something bigger, it's because you are. At Amgen, our shared mission—to serve patients—drives all that we do. It is key to our becoming one of the world's leading biotechnology companies. We are global collaborators who achieve together—researching, manufacturing, and delivering ever-better products that reach over 10 million patients worldwide. It's time for a career you can be proud of.

In this vital role, the LM ML Standards & Governance Specialist will play a key part in establishing the foundational standards, governance processes, and operational practices required to scale AI and machine learning capabilities across Large Molecule Discovery (LMD). This role will help ensure that machine learning assets, code repositories, documentation, data dependencies, and deployment practices are reproducible, secure, compliant, and aligned with enterprise requirements.

Working closely with AI/ML scientists, software engineers, data engineers, informatics teams, and enterprise technology partners, this individual will help define and implement governance frameworks that support the responsible development, deployment, and maintenance of AI-enabled scientific solutions. The role will serve as a bridge between scientific innovation and operational rigor, ensuring that emerging AI capabilities can be effectively maintained, audited, and scaled across research workflows.

This position is well suited for someone who enjoys building structure around complex technical ecosystems and has experience supporting machine learning operations, technical governance, software lifecycle management, or scientific platform administration. Core responsibilities include:

• Establish standards for machine learning code, documentation, model artifacts, and supporting technical assets
• Define and implement best practices for model lifecycle management, version control, release management, and reproducibility
• Partner with scientists to ensure AI workflows can be validated, reproduced, and maintained over time
• Support management of shared repositories, technical assets, and documentation systems
• Collaborate with technology teams to implement scalable governance, repository, and lifecycle management practices
• Align AI development practices with enterprise security, compliance, and technology expectations
• Translate scientific requirements into operational standards that enable sustainable AI adoption
• Drive consistency and reuse of AI assets across the Large Molecule Discovery organization

Basic Qualifications:

Doctorate degree PhD

Or

Master's degree and 8+ years of directly related experience

Or

Bachelor's degree and 10+ years of directly related experience

Preferred Qualifications:

• Experience supporting machine learning platforms, MLOps environments, or AI governance programs
• Understanding of machine learning lifecycle management and model deployment practices
• Experience implementing standards for model documentation, validation, and reproducibility
• Familiarity with source control systems, CI/CD pipelines, and software development lifecycle practices
• Experience working in biotechnology, pharmaceutical, life sciences, or research environments
• Strong organizational, documentation, and stakeholder management skills
• Ability to balance governance requirements with practical scientific needs

What you can expect of us:

As we work to develop treatments that take care of others, we also work to care for our teammates' professional and personal growth and well-being.

• 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 and bi-annual company-wide shutdowns
• Flexible work models, including remote work arrangements, where possible