AI Engineer (AI Accelerator Program)

UCSF•San Francisco, CA

7h•$130,000 - $165,000•Hybrid

About The Position

UCSF Health is seeking an AI Engineer to design, build, and deploy scalable AI-driven applications that improve clinical operations, patient care, and health system efficiency. The position spans the AI lifecycle, including data pipeline development, model training and evaluation, and deployment of machine learning and generative AI solutions on cloud platforms. The engineer translates emerging technologies, including large language models, into production-ready tools integrated with healthcare systems, and develops APIs or web applications for clinical and operational workflows. The role collaborates with clinicians, data scientists, researchers, and IT teams to deliver reliable, secure, and scalable solutions. The Health AI team is part of UCSF Health IT and supports the development, implementation, and monitoring of AI, machine learning, and analytical tools. Its multidisciplinary expertise includes data science, machine learning, software and data engineering, business, nursing informatics, and medical informatics. The position requires one day onsite per week; that frequency may increase based on business needs. Certain employment terms, including pay and benefits, are subject to negotiation with the appropriate union.

Requirements

Bachelor’s degree in a related area and/or equivalent experience or training.
Five years of experience in progressively responsible positions designing, implementing, and maintaining complex AI/ML applications.
Experience with data analysis and machine learning tools such as Jupyter, Pandas, scikit-learn, NumPy/SciPy, and PyTorch.
Advanced knowledge of the full software development lifecycle.
Advanced Python experience, including writing clean, efficient, production-level code.
Advanced SQL experience, such as SQL Server or PostgreSQL.
Experience deploying, monitoring, and maintaining AI/ML models and pipelines.
Experience designing and developing APIs or microservices for AI/ML applications.
Familiarity with web application frameworks such as React and JavaScript/TypeScript, or integrating backend systems with user-facing applications.
Experience with large language models, including prompt engineering, evaluation, and production deployment.
Experience with real-time or streaming data processing systems and low-latency inference architectures.
Effective communication and interpersonal skills, including the ability to communicate technical information to technical and non-technical personnel at different organizational levels.
Ability to work independently and collaboratively, learn effectively, meet deadlines, and solve problems broadly.
Ability to interface regularly with management.
Strong project leadership and management skills.

Nice To Haves

Master’s degree or PhD in Computer Science, Computer Engineering, or a related area, and/or equivalent experience or training.
Experience with Epic Clarity or the Clinical Data Model.
Familiarity with data visualization tools such as Tableau.
Experience with Epic data structures.

Responsibilities

Plan, design, develop, modify, debug, deploy, and evaluate complex software; analyze existing systems, formulate logic and algorithms, conduct data analysis, and apply security practices.
Specify and execute test plans; develop conversion and implementation plans; conduct data modeling, performance and integration testing; build interfaces; and establish source-code control and configuration management approaches.
Prepare and approve or obtain approval for system and programming documentation, oversee changes to development and maintenance standards, and set technical requirements for software specifications.
Apply industry practices, community standards, and department policies; potentially serve as technical lead for projects or lead software development professionals.
Build and maintain data integration and processing/transformation pipelines using SQL databases or APIs to support AI/ML tools.
Build systems for implementation, monitoring, and maintenance of AI/ML tools using MLOps practices, including CI/CD, model versioning, and cloud-native deployment.
Collaborate with data scientists and researchers to implement monitoring of AI/ML tools for safety, bias or drift, performance, and validity.
Design and develop APIs, services, or lightweight web applications that integrate AI/ML and generative AI into clinical and operational workflows.
Develop and deploy generative AI solutions, including LLM-based systems, prompt engineering, retrieval-augmented generation, and evaluation frameworks for unstructured data.
Design and implement real-time or near-real-time data processing and inference systems, including scalable event-driven architectures and reliable, low-latency integration into production workflows.