Machine Learning Specialist

The Machine Learning Specialist plays a key role in advancing UCLA Health's AI and machine learning capabilities. This position contributes to the development, evaluation, testing, and validation of AI/ML models that support datadriven decisionmaking across clinical, financial, and operational domains. In addition to model development, the specialist will help enhance MLOps processes, support the adoption of governance standards, and develop content and best practices that strengthen UCLA Health's enterprise AI/ML program.

This role requires the ability to blend domain knowledge with software engineering, data science, and modern ML engineering practices to solve complex challenges using structured and unstructured healthcare data.

• Partner with stakeholders to understand AI/ML objectives and translate them into appropriate tools, design patterns, and solution approaches.
• Define, document, and communicate components of the AI/ML platform to internal and external partners.
• Work with ML Engineers to identify team and departmental requirements for an effective and scalable AI/ML platform.
• Collaborate effectively with crossfunctional teams including data scientists, domain experts, data engineers, BI developers, and operational stakeholders.
• Identify opportunities to leverage AI/ML for process optimization, predictive modeling, and operational insights.
• Develop AI/ML prototypes and proofofconcepts aligned with project specifications.
• Acquire, clean, and preprocess datasets for model training, testing, and validation.
• Ensure all models and workflows adhere to UCLA Health Responsible AI standards, including documentation, bias assessments, and governance requirements.
• Provide testing, troubleshooting, and support during model development and stakeholder engagement.
• Ensure metadata is captured and integrated throughout all stages of the AI/ML lifecycle.

• Collaborate with ML Engineers to design, build, and evaluate models tailored to specific business needs and available data.
• Select appropriate algorithms, architectures, and techniques based on use case and data characteristics.
• Optimize models for performance, scalability, interpretability, and ongoing maintainability.
• Work with OHIA and UCLA Health IT teams to integrate models into operational systems such as CareConnect.
• Develop and maintain tools and techniques for monitoring model performance and drift in production environments.

• Stay current on the latest developments in AI, ML, MLOps, LLMs, and related fields.
• Propose and implement enhancements to existing models, workflows, and platform components based on emerging technologies and research findings.
• Contribute to the ongoing development of ML Engineering and Data Governance Engineering team practices, standards, and processes.