SUMMARY STATEMENT: 

This internship is embedded within UCLA Health Information Technology’s Office of Health Informatics and Analytics Teams, supporting analytics and AI/ML use cases across clinical, operations, finance, quality, and research domains.  The Student Intern will gain hands on experience across the end to end data and AI lifecycle, including data engineering pipelines, feature platforms, MLOps practices, and high-performance computing (HPC) environments using cloud based technologies such as Azure, AWS and Databricks.

Internship Objectives

By the end of the program, interns will:

• Contribute production‑ready code to data, ML, or infrastructure platforms
• Understand how enterprise AI/ML systems are designed, deployed, and governed in healthcare
• Collaborate with data engineers, ML engineers, architects, and researchers
• Deliver tangible artifacts aligned with UCLA Health analytics initiatives

Key Focus Areas

Interns will work in one or more of the following areas, based on interest and team needs:

Data Analytics, Architecture & Engineering

• Building Core data products and reusable data pipelines
• Data orchestration workflows and APIs
• Data quality and observability foundations

ML Engineering & MLOps

• Feature engineering and feature store development
• CI/CD for machine learning workflows
• Monitoring, maintenance, and retraining of production ML models
• Collaboration with data scientists to operationalize models

Compute & Research Infrastructure

• Cloud platforms and HPC environments
• AI/ML workloads for clinical and research analytics
• Trusted research environments (e.g., ULEAD)

10–12 Week Deliverables

By the conclusion of the internship, each intern is expected to deliver:

1. A Production‑Grade Technical Artifact
• Data pipeline, ML feature module, API, HPC configuration, or infrastructure component
2. Documentation & Knowledge Transfer
• Technical documentation explaining design decisions, usage, and operational considerations
3. Quality & Reliability Contributions
• Data quality checks, observability metrics, CI/CD integration, or validation scripts
4. Final Presentation or Demo
• Walkthrough of project outcomes, lessons learned, and future improvement opportunities
5. Code Contribution to Team Repositories
• Reviewed, tested, and version‑controlled code aligned with team standards