AI Engineer (AI Accelerator Program)

UCSF Health; San Francisco (CA); Hybrid; USD 140,000 - 210,000; Full time

Job summary

UCSF Health is seeking a skilled AI Engineer to design, build, and deploy scalable AI‑driven applications that improve clinical operations and patient care. You will work across data pipelines, model training, evaluation, and cloud deployment to deliver reliable tools for workflows.

You will translate LLMs into production‑ready solutions, develop APIs, and implement robust monitoring for safety and bias. Collaboration with clinicians and IT teams is essential to success.

Qualifications

• 5 years of experience designing and maintaining AI/ML applications.
• Strong Python programming and production-grade code skills.
• Experience with data analysis tools (Jupyter, Pandas, NumPy).
• Hands-on ML model deployment and monitoring in production.
• Ability to design APIs or microservices for AI/ML use cases.
• Excellent communication and collaboration across teams.

Responsibilities

• Design, build, and deploy scalable AI-driven applications for clinical ops and care.
• Develop data pipelines, model training, evaluation, and cloud deployment.
• Create APIs or web apps enabling AI/ML in workflows.
• Implement MLOps practices: CI/CD, model versioning, monitoring.
• Collaborate with clinicians, data scientists, and IT teams.

Skills

Python

SQL

LLMs

MLOps

API development

Data analysis

Production-grade code

Communication

Team collaboration

Education

Bachelor's degree

Master’s degree or PhD (preferred)

Tools

Jupyter

Pandas

scikit-learn

NumPy/SciPy

PyTorch

Job description

This position requires 1 day onsite per week, based on business needs; this may increase.

UCSF Health is seeking a highly skilled AI Engineer to design, build, and deploy scalable AI‑driven applications that improve clinical operations, patient care, and health system efficiency. This role spans the full AI lifecycle, including data pipeline development, model training and evaluation, and deployment of machine learning and generative AI solutions on modern cloud platforms.

The AI Engineer will translate emerging technologies, including large language models (LLMs), into production‑ready tools that integrate with healthcare systems. Responsibilities include building robust data pipelines, deploying machine learning and generative AI models, and developing APIs or web‑based applications that enable seamless use in clinical and operational workflows. This role works closely with clinicians, data scientists, and IT teams to deliver solutions that are reliable, secure, and scalable.

Responsibilities

• Apply advanced software concepts to plan, design, develop, modify, debug, deploy and evaluate highly complex software for functional areas; analyze existing complex software or develop logic and algorithms for new systems; perform data analysis and testing; apply and enforce complex programming security practices.
• Specify, develop and execute complex test plans; develop conversion and system implementation plans; perform or direct data modeling, performance and integration testing; build interfaces; determine source code control techniques and configuration management design and changes.
• Prepare and approve system and programming documentation; initiate and oversee changes in development, maintenance, and system standards; set technical requirements for complex software specifications.
• Understand and apply industry practices, community standards, and departmental policies; serve as technical lead for multiple software development projects; lead a team of software development professionals; enforce project plans.
• Build and maintain data integration with SQL databases or APIs and data processing and transformation pipelines to support AI/ML tools.
• Identify and build systems for implementation, monitoring, and maintenance of AI/ML tools using modern MLOps practices including CI/CD, model versioning, and cloud‑native deployment.
• Collaborate with data scientists and researchers to design and implement metrics and processes to automatically monitor AI/ML tools for safety, bias or drift, performance, and validity.
• Design and develop APIs, services, or lightweight web applications to enable integration of AI/ML and generative AI capabilities into clinical and operational workflows.
• Develop and deploy generative AI solutions (e.g., LLM‑based systems), including prompt engineering, retrieval‑augmented generation (RAG), and evaluation frameworks for unstructured data.
• Design and implement highly complex real‑time or near real‑time data processing and inference systems to support AI/ML and generative AI applications; develop scalable, event‑driven architectures and ensure reliable, low‑latency integration of AI capabilities into production clinical and operational workflows.

Qualifications

Required Qualifications

• Bachelor's degree in a related area and/or equivalent experience or training.
• 5 years of experience in positions of increasing responsibility designing, implementing, and maintaining complex AI/ML applications.
• Experience with data analysis and machine learning tools such as Jupyter, Pandas, scikit‑learn, NumPy/SciPy, PyTorch, etc.
• Demonstrated advanced knowledge of the full software development lifecycle.
• Advanced experience with Python; ability to write clean, efficient, production‑level code.
• Advanced experience with SQL (e.g., SQLServer, PostgreSQL).
• Demonstrated experience deploying, monitoring, and maintaining AI/ML models and pipelines.
• Experience designing and developing APIs or microservices to support AI/ML applications.
• Familiarity with web application development frameworks (e.g., React, JavaScript/TypeScript) or integrating backend systems with user‑facing applications.
• Experience with large language models (LLMs), including prompt engineering, evaluation, and production deployment.
• Experience with real‑time or streaming data processing systems and low‑latency inference architectures.
• Demonstrated effective communication and interpersonal skills.
• Demonstrated ability to communicate technical information to technical and non‑technical personnel at various levels in the organization.
• Self‑motivated and works independently and as part of a team; able to learn effectively and meet deadlines.
• Demonstrated broad problem‑solving skills.
• Demonstrated ability to interface with management on a regular basis.
• Excellent project leadership and management skills.

Preferred Qualifications

• Master’s degree or PhD in Computer Science, Computer Engineering, or related area and/or equivalent experience or training.
• Epic Clarity or Clinical Data Model experience.
• Familiarity with data visualization tools (e.g., Tableau).
• Experience with Epic data structures.

Equal Employment Opportunity

The University of California is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, protected veteran status, or other protected status under state or federal law.

Salary Information

The final salary and offer components are subject to additional approvals based on UC policy. Your placement within the salary range is dependent on a number of factors including your work experience and internal equity for this position classification at UCSF. For positions represented by a labor union, placement within the salary range will be guided by the collective bargaining agreement.