Description
UCLA HEALTH
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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 handsâon experience across the endâtoâend data and AI lifecycle, including data engineering pipelines, feature platforms, MLOps practices, and highâperformance computing (HPC) environments using cloudâbased technologies such as Azure, AWS and Databricks.
By the end of the program, interns will:
Contribute productionâready 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, productionâoriented engineering teams
- Curious, detailâoriented, and motivated to learn enterpriseâscale 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