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General Information

Work Location: Los Angeles, CA, USA
Onsite or Remote
Flexible Hybrid
Work Schedule
Monday -Friday 8 A.M. -5 P.M
Posted Date
04/13/2026
Salary Range: $17.9 - 47 Hourly
Employment Type
5 - Student: Casual/Restricted
Duration
10 Weeks
Job #
29672

Primary Duties and Responsibilities

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.

Job Qualifications

Required:

  • Currently pursuing a undergraduate 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
  • Python, SQL, and Java for data engineering and ML 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, model deployment and monitoring
  • Data Engineering Foundations
  • Building pipelines, reusable workflows, APIs, and data quality mechanisms
  • High Performance Computing & Infrastructure
  • Exposure 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.