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Summary Statement

This internship is embedded within UCLA Health Information Technology’s Office of Health Informatics and Analytics, 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. Interns may also contribute to applied AI development and evaluation efforts, including generative AI experimentation, model validation, and responsible AI practices within healthcare analytics workflows.

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

•             Gain exposure to applied data science workflows, including exploratory analysis, machine learning experimentation, and evaluation of AI model outputs

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

•             Developing data orchestration workflows and APIs

•             Establishing data quality and observability foundations

ML Engineering & MLOps

•             Feature engineering and feature store development

•             CI/CD pipelines for machine learning workflows

•             Monitoring, maintenance, and retraining of production ML models

•             Collaborating with data scientists to operationalize models

AI Development & Data Science

•             Exploring machine learning and generative AI approaches to healthcare analytics challenges

•             Conducting exploratory data analysis and experimentation with AI/ML models

•             Developing evaluation frameworks and metrics for AI model performance

•             Contributing to responsible AI practices, including bias assessment, validation, and model evaluation

•             Supporting prototyping and experimentation for emerging AI use cases across UCLA Health

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:

Production-Grade Technical Artifact

Data pipeline, ML feature module, API, HPC configuration, or infrastructure component or AI/ML experimentation framework

Documentation & Knowledge Transfer

Technical documentation explaining design decisions, usage, and operational considerations

Quality & Reliability Contributions

Data quality checks, observability metrics, CI/CD integration, or validation scripts or AI model evaluation artifacts

Final Presentation or Demo

Walkthrough of project outcomes, lessons learned, and future improvement opportunities

Code Contribution to Team Repositories

Reviewed, tested, and version-controlled code aligned with team standards