Job Description

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.