Job Description

General Information

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Work Location: Los Angeles, CA, USA
Onsite or Remote
Flexible Hybrid
Work Schedule
Monday-Friday 8am-5pm
Posted Date
04/13/2026
Salary Range $17.9 - 47 Hourly
Employment Type
1 - Staff: Contract
Duration
10 weeks.
Job #
29673

Primary Duties and Responsibilities

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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

• Technical documentation explaining design decisions, usage, and operational considerations

• Data quality checks, observability metrics, CI/CD integration, or validation scripts

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

• Reviewed, tested, and version‑controlled code aligned with team standards

Job Qualifications

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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

oPython, SQL, and Java for data engineering and ML development

·Cloud & Data Platforms

oExperience or interest in Azure and Databricks for analytics and ML workloads

·Machine Learning & MLOps Concepts

oFeature engineering, feature stores, CI/CD, model deployment and monitoring

·Data Engineering Foundations

oBuilding pipelines, reusable workflows, APIs, and data quality mechanisms

·High Performance Computing & Infrastructure

oExposure to HPC, AI/ML compute environments, and research infrastructure

As a condition of employment, the final candidate who accepts an offer of employment will be required to disclose if they have been subject to any final administrative or judicial decisions within the last seven years determining that they committed any misconduct; or have filed an appeal of a finding of substantiated misconduct with a previous employer.