Machine Learning Specialist

Primary Duties and Responsibilities

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 data‑driven decision‑making 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 cross‑functional 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 proof‑of‑concepts 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 Care Connect.
• 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.

This is a flex-hybrid role which will require you to be onsite at least 10% of the time, within 48 hours of being asked to come on-site, and as required by operational need; there are no reimbursements for travel to "home office" location. Each employee must complete a Flex Work Agreement with their manager which will outline arrangement parameters and aids both parties in fully understanding expectations.

Arrangements are regularly evaluated, and are subject to termination.

Salary offers are determined based on various factors including, but not limited to, qualifications, experience, and equity. The full salary range for this position is $86,400 - $184,800 annually. The budgeted salary or hourly range that the University reasonably expects to pay for this position is approximately between the start and midpoint.

• Bachelor’s degree in computer science, information systems, engineering, or a related field - Required
• Familiarity with applying machine learning techniques and concepts within practical implementations in healthcare domains preferred
• Experience with ML frameworks and libraries such as Tensor Flow, PyTorch, scikit‑learn, etc.
• Experience with Databricks, LLMs, and other generative AI technologies preferred
• Experience with ETL/ELT, data warehousing/data marts and exposure to data services, API development…