Job Description

Career Category Engineering

HOW MIGHT YOU DEFY IMAGINATION?
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.
Live | What you will do

• Build, test, and maintain reusable components for ML pipelines and data science use cases in collaboration with senior engineers and data scientists.
• Develop model tracking, versioning, and deployment workflows using MLflow and Databricks.
• Support data scientists across teams with infrastructure needs, connector usage, and dataset preparation.
• Help prototype and deploy internal tools (e.g., Streamlit apps) to visualize model results or enable user interactions.
• Follow engineering best practices around version control, documentation, and testing in shared repositories.
• Collaborate with enterprise engineering and data platform teams to ensure compliance with cloud architecture, data privacy, and governance standards.

Thrive | What you can expect
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.
Basic Qualifications

• Bachelor's or Master's in Computer Science, Data Engineering, or related technical field.
• 3-5 years of experience in ML engineering, data engineering, or DevOps supporting ML pipelines.
• Proficiency in Python and SQL, with working knowledge of Databricks, MLflow, and AWS (S3, EC2, Lambda).
• Experience supporting model deployment, monitoring, and packaging workflows.
• Ability to document work clearly and collaborate effectively with cross-functional teams.

Preferred Qualifications

• Familiarity with vector stores, embedding pipelines, or RAG-style use cases.
• Experience developing or supporting Streamlit or Dash-based apps for data products.
• Working knowledge of Git-based workflows, containerization, and CI/CD pipelines.

Passion for enabling data science teams and driving engineering quality.
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Skills Required