Job Description

General Information

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Work Location: Los Angeles, CA, USA
Onsite or Remote
Flexible Hybrid
Work Schedule
Monday-Friday, 8:00 am - 5:00 pm
Posted Date
02/13/2026
Salary Range $86400 - 184800 Annually
Employment Type
2 - Staff: Career
Duration
Indefinite
Job #
28645

Primary Duties and Responsibilities

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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.

Key Responsibilities

AI/ML Engineering & Platform Development

• 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.

Model Development, Deployment & Monitoring

• 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.

Continuous Improvement & Research

• 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 FlexWork 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.

Job Qualifications

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•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 TensorFlow, 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 and REST architecture

•Experience in analysis, data relationships, data aggregation, and summarization with the ability to consolidate data from disparate systems is preferred.

•Experience with integrating Artificial Intelligence & Machine Learning governance tools, processes, and frameworks

•1 – 3 years of experience with Python programming required

•1 – 3 years of SQL programming skills, particularly for Oracle, Microsoft SQL Server, and Databricks (ANSI) SQL preferred

•Exposure to software product configuration, requirements capture, configuration and deployment

•Experience with software implementation methodologies, Agile, SCRUM

•Experience with software Development Lifecycle and environment promotion

•Ability to comprehend relationships among data field elements in the Epic electronic health record, such as flowsheets, notes, encounters, lab results, billing, and paid claims.

•1 – 3 years of work experience with health care data is preferred

•Some exposure with software implementation of healthcare data

•Ability to communicate clearly and effectively with business partners and business experts

•Strong attention to detail, along with effective problem solving and analytical skills

As a condition of employment, the final candidate who accepts a conditional offer of employment will be required to disclose if they have been subject to any final administrative or judicial decisions within the last seven years determining that they committed any misconduct; received notice of any allegations or are currently the subject of any administrative or disciplinary proceedings involving misconduct; have left a position after receiving notice of allegations or while under investigation in an administrative or disciplinary proceeding involving misconduct; or have filed an appeal of a finding of misconduct with a previous employer.