Description
UCLA HEALTH
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Description
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
Qualifications
Required:
- Currently pursuing a degree in Computer Science, Data Science, Engineering, or a related field
- Strong interest in data engineering, AI/ML, or compute infrastructure
- Comfortable working in collaborative, productionâoriented engineering teams
- Curious, detailâoriented, and motivated to learn enterpriseâscale systems in healthcare
Desired Technical Skills
Programming Languages
- Python, SQL, and Java for data engineering and machine learning development
Cloud & Data Platforms
- Experience or interest in Azure and Databricks for analytics and ML workloads
Machine Learning & MLOps Concepts
- Feature engineering, feature stores, CI/CD pipelines, model deployment, and monitoring
Applied Data Science & AI
- Experience or interest in machine learning experimentation, natural language processing (NLP), or generative AI tools
- Familiarity with ML libraries such as scikit-learn, PyTorch, or similar frameworks is a plus
Data Engineering Foundations
- Building data pipelines, reusable workflows, APIs, and data quality mechanisms
High-Performance Computing & Infrastructure
- Exposure to HPC environments, AI/ML compute platforms, and research infrastructure