Job Information
UCLA Health Undergraduate Summer Intern -UCLA Health Information Technology's Advanced Analytics (Data Science) Team in Los Angeles, California
Description
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:
A Production‑Grade Technical Artifact
Data pipeline, ML feature module, API, HPC configuration, or infrastructure component
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
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, production‑oriented engineering teams
Curious, detail‑oriented, and motivated to learn enterprise‑scale systems in healthcare
Desired Technical Skills
· Programming Languages
o Python, SQL, and Java for data engineering and ML development
· Cloud & Data Platforms
o Experience or interest in Azure and Databricks for analytics and ML workloads
· Machine Learning & MLOps Concepts
o Feature engineering, feature stores, CI/CD, model deployment and monitoring
· Data Engineering Foundations
o Building pipelines, reusable workflows, APIs, and data quality mechanisms
· High Performance Computing & Infrastructure
o Exposure to HPC, AI/ML compute environments, and research infrastructure
UCLA Health welcomes all individuals, without regard to race, sex, sexual orientation, gender identity, religion, national origin or disabilities, and we proudly look to each person’s unique achievements and experiences to further set us apart.