Undergraduate Summer Intern – OHIA ML Engineering Full Time NEW

Description

Overview: The OHIA ML Engineering team is directly involved in all aspects of the AI/ML lifecycle, from interfacing with data scientists, writing code for production, and monitoring, maintaining, and retraining existing production models. The team uses a combination of SQL, Python, and Java for their software development and ML projects. The OHIA ML Engineering team also is responsible for developing and socializing OHIA’s MLOps practices across the UCLA Health IT (UHIT) organization.

Potential Projects: Summer interns are encouraged to participate in projects of their interest on OHIA’s Project roadmap. Projects available can be in areas of analytics delivery, data governance, AI usage, cloud strategy, and more.

Interns 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.

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

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.