Undergraduate Summer Intern - OHIA ML Engineering

Overview:The OHIAML Engineering team is directly involved inallaspects of theAI/ML lifecycle, from interfacing with datascientists, writing code for production,and monitoring, maintaining,and retraining existing production models. The team usesacombination of SQL, Python,and Javafor their software developmentand ML projects. The OHIAML Engineering teamalso is responsible for developingand socializing OHIA's MLOps practicesacross the UCLAHealth 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 handson experience across the endtoend data and AI lifecycle, including data engineering pipelines, feature platforms, MLOps practices, and highperformance computing (HPC) environments using cloudbased technologies such as Azure, AWS and Databricks.

By the end of the program, interns will:

Contribute productionready 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

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, productionoriented engineering teams
• Curious, detailoriented, and motivated to learn enterprisescale 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