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The Vice President and Chief Health AI Officer (CHAIO) serves as the senior physician executive responsible for establishing and advancing the health system strategic vision, governance framework, and operational execution of Artificial Intelligence (AI) across UCSF Health. Reporting directly to the Chief Information Officer for Health (Health CIO), the CHAIO serves as a key member of the Health IT executive leadership team and partners closely with clinical, operational, research, academic, and technology leaders to accelerate the safe, ethical, and effective adoption of AI in support of UCSF Health’s mission of exceptional patient care, education, research, and innovation. Working in close partnership with the UCSF Schools of Medicine, Nursing, Pharmacy, and Dentistry, the CHAIO helps ensure that Health AI strategies support the educational mission by preparing current and future healthcare professionals to effectively, safely, and ethically incorporate AI into clinical practice while recognizing the unique needs of learners participating in patient care and clinical training environments. The incumbent is a licensed physician with recognized expertise in clinical practice, healthcare operations, informatics, and Artificial Intelligence, serving as the primary executive advisor to UCSF Health leadership on the clinical application of AI technologies.
The CHAIO is responsible for developing and executing a comprehensive enterprise AI strategy that aligns with organizational priorities and advances clinical quality, patient safety, operational efficiency, workforce effectiveness, and financial stewardship. This executive serves as the health system’s primary authority for AI strategy, standards, governance, and implementation, ensuring that AI initiatives deliver measurable value and are aligned with UCSF Health’s strategic objectives.
The CHAIO leads the development of the UCSF Health enterprise AI ecosystem, including the technology infrastructure, data assets, governance processes, and organizational capabilities necessary to enable responsible and scalable AI adoption. Working collaboratively with Health IT, Informatics, Data and Analytics, Infrastructure, Cybersecurity, Compliance, Legal, Risk Management, and Clinical Operations, the CHAIO ensures that AI solutions are secure, interoperable, compliant with applicable regulations, and aligned with industry best practices and ethical principles.
This position is accountable for establishing and facilitating AI governance structures, including executive committees, review boards, and decision\-making frameworks that oversee prioritization, evaluation, implementation, and ongoing monitoring of AI technologies. The CHAIO develops policies and standards that promote responsible AI use, emphasizing transparency, fairness, accountability, explainability, privacy, and security while mitigating risks associated with bias, discrimination, and inappropriate use of AI technologies. While collaborating with Health System leadership, the CHAIO hold authority to prioritize, approve, or halt AI initiatives, including authority to pause unsafe clinical AI.
The CHAIO collaborates with health system leadership to identify, prioritize, and implement AI\-enabled solutions that improve patient outcomes, enhance clinician and staff experiences, optimize operational performance, and support organizational transformation. The incumbent partners with business, clinical, and informatics leaders to translate complex organizational challenges into practical AI\-driven solutions and ensures alignment between enterprise AI investments and institutional priorities.
The CHAIO is the executive owner for Health AI strategic partnerships, including strategy, evaluation, selection, governance and value realization of strategic third\-party AI vendor relationships and co\-development partnerships. The CHAIO helps work with operational and IT leaders to manage the lifecycles of these operational deployments. Where AI is embedded in or adjacent to enterprise application platforms, the CHAIO will collaborate with technology and business leaders to prioritize AI strategy, model selection, and clinical performance of those capabilities.
The CHAIO also partners with clinical education leaders to promote Health AI literacy, responsible AI competencies, and workforce readiness across the organization. The incumbent ensures that Health AI implementations appropriately account for the educational environment, supporting trainees during clinical rotations while preserving patient safety, educational quality, appropriate supervision, and equitable learning opportunities.
As the executive leader for Health AI, the CHAIO represents UCSF Health in governance activities related to enterprise data, analytics, infrastructure architecture, cybersecurity, and emerging technologies. The incumbent works closely with UCSF and UC system partners, clinical research leaders, external healthcare organizations, industry collaborators, academic institutions, and technology vendors to advance UCSF Health’s leadership position in AI innovation and establish strategic partnerships that accelerate organizational capabilities. The CHAIO collaborates with clinical research leadership to facilitate the responsible application of Health AI in clinical trials, translational science, real\-world evidence generation, and learning health system initiatives, ensuring that AI\-enabled research aligns with institutional governance, regulatory requirements, and ethical standards.
The CHAIO serves as Co\-Chair of the UCSF Health Converge AI Accelerator Program, providing strategic direction, establishing program priorities, evaluating opportunities, and aligning accelerator initiatives with health system objectives. The incumbent collaborates with executive leadership, academic partners, technology companies, AI startups, venture capital organizations, and innovation stakeholders across the San Francisco Bay Area to identify and advance high\-value AI initiatives. The CHAIO leverages UCSF’s unique position within one of the world’s leading AI and technology ecosystems to cultivate strategic relationships that accelerate innovation, facilitate responsible technology adoption, and strengthen UCSF Health’s leadership in healthcare AI.
The CHAIO also serves as a key liaison, with the opportunity to hold a faculty appointment, to the UCSF Division of Clinical Informatics and Technology (DoC\-IT) and other institutional partners to ensure effective collaboration and alignment across AI\-related programs and initiatives.
The CHAIO continuously evaluates the rapidly evolving AI landscape, emerging technologies, regulatory developments, and industry trends to ensure UCSF Health remains at the forefront of innovation. This executive assesses the feasibility, value, and risk of emerging AI capabilities and advises senior leadership on strategic investments and adoption opportunities that support long\-term organizational growth and competitive advantage. The CHAIO serves as a visible ambassador for UCSF Health within the national Health AI community (particularly in the Bay Area), developing trusted relationships with technology leaders, entrepreneurs, venture investors, academic collaborators, and public\-sector organizations to foster collaboration, influence emerging healthcare AI practices, and identify opportunities that advance UCSF Health’s strategic priorities.
The CHAIO is responsible for developing the organizational capabilities necessary to successfully scale AI across the enterprise. This includes building and leading high\-performing multidisciplinary teams, attracting and developing AI talent, establishing strategic partnerships, and leveraging external expertise when appropriate. The incumbent fosters a culture of innovation, collaboration, continuous learning, and responsible AI adoption throughout the organization.
The CHAIO establishes measurable performance indicators and outcomes for AI initiatives and leverages enterprise data and analytics capabilities to evaluate effectiveness, track value realization, and communicate progress to executive leadership and key stakeholders. Through strategic leadership and operational oversight, the CHAIO ensures that AI investments produce measurable improvements in patient care, clinical outcomes, operational performance, workforce productivity, clinical research and innovation, research advancement, and organizational effectiveness.
Overall, the Vice President and Chief Health AI Officer provides visionary leadership and enterprise\-wide accountability for the responsible adoption and advancement of Artificial Intelligence at UCSF Health, serving as the strategic bridge between people, processes, data, technology, and innovation to transform healthcare delivery and improve the health of the communities UCSF serves.
Department Overview
The VP/Chief Health AI Officer (CHAIO)will report to the UCSF Health Chief Information Officer who is responsible as the primary executive leader of information technology at UCSF Health (referred to as UCSF Health IT). This involves certain direct reporting responsibilities including administrative applications, IT infrastructure, customer support, data and analytics, IT security, IT program management and IT Governance for UCSF. This organization is responsible to provide the foundational IT and data and analytics necessary to support the core mission areas and administrative operations at UCSF. UCSF also has a substantial departmental IT and analytics presence with which the UCSF IT organization collaborates closely through formal and information structures. This construct enables close alignment with specialized departmental needs while promoting leverage of core information technology assets and capabilities. A significant shift in operating model is under way at UCSF which will leave the distributed model in place but align IT, departmental and other functional areas within cross\-functional teams focused on the technology and innovation needs of key business units and departments at UCSF. This transformation will evolve over the next 3 years and represents an important aspect of the UCSF drive to innovate using data and technology as key enablers of improved patient, student, faculty, physician and researcher experience.
*Knowledge, Skills and Abilities* *Req / Pref*
Recognized expertise in clinical practice, healthcare operations, clinical informatics, and the responsible application of Artificial Intelligence to improve patient care and health system performance. Req
Demonstrated ability to develop and execute enterprise clinical AI strategies aligned with organizational priorities, patient outcomes, and healthcare transformation. Req
Expert knowledge of AI governance, clinical safety, AI ethics, regulatory considerations, privacy, bias mitigation, and responsible AI implementation in healthcare. Req
Demonstrated ability to evaluate, prioritize, and oversee clinical AI initiatives across multiple specialties and operational settings while balancing innovation, patient safety, and organizational risk. Req
Exceptional executive leadership, relationship\-building, and influence skills with the ability to engage physicians, clinicians, executives, researchers, educators, and technology leaders across a complex academic health system. Req
Demonstrated success leading multidisciplinary teams composed of physicians, informaticists, operational leaders, data scientists, technology professionals, and clinical staff. Req
Outstanding verbal and written communication skills, including the ability to communicate complex AI concepts, clinical risks, and strategic recommendations to executive leadership, governing bodies, and diverse stakeholder groups. Req
Proven ability to lead large\-scale organizational change, build physician engagement, and accelerate adoption of innovative technologies across complex healthcare environments. Req
Deep understanding of healthcare delivery, patient safety, quality improvement, clinical operations, academic medicine, and healthcare workforce transformation. Req
Demonstrated ability to translate emerging AI capabilities into clinically meaningful, scalable solutions that improve patient outcomes, clinician experience, and operational performance. Req
Strong understanding of clinical decision support, predictive analytics, ambient documentation, generative AI, and other AI applications relevant to healthcare delivery. Req
Experience establishing governance structures, executive committees, and decision\-making frameworks that oversee the evaluation, deployment, monitoring, and lifecycle management of clinical AI technologies. Req
Demonstrated experience developing strategic partnerships with healthcare organizations, academic institutions, industry partners, and technology vendors to advance clinical AI innovation. Req
Strong business acumen with the ability to evaluate AI investments, measure value realization, and align AI initiatives with organizational strategy and financial stewardship. Req
Innovative and visionary leader with demonstrated ability to anticipate emerging trends in healthcare AI and guide organizations through rapid technological change. Req
8\+ years of progressive physician leadership experience leading clinical AI, clinical informatics, digital transformation initiatives, or a related function within a health system or academic medical center. Req
Current or prior clinical practice experience as a licensed physician with demonstrated credibility among practicing clinicians and the ability to lead physician engagement and adoption of AI\-enabled clinical transformation. Req
Five (5\) years of executive leadership experience (Chief Medical Informatics Officer, Chief Clinical Informatics Officer, Chief AI Officer, or comparable physician executive role) physician leadership role leading clinical AI, clinical informatics, or digital transformation initiatives within a health system or academic medical center. Pref
Education, Licenses and Certifications:
*List Education, Licenses and Certifications a candidate must possess or meet to be considered for the position. You may also select any of these attributes as being preferred.* *These will be included in the job posting/advertisement and will be used to screen applicants.*
*Education*
*Req / Pref*
*MD required.* Req
*Licenses*
*Req / Pref*
*Active, unrestricted license to practice medicine with eligibility for physician licensure in the state of California.* *Req*
*Certifications*
*Req / Pref*
*Board certified in Clinical Informatics.* *Pref*
Special Conditions of Employment: (Statements identifying the fundamental non\-negotiable job conditions and/or requirements which an individual must meet to be eligible for the position. For example, the ability to pass a background check, work in a particular environmental setting, work a flexible or irregular work schedule, etc.)
Must pass a background check.
*
Management of Funds :
Does this position require oversight or management funds? If No : Please skip this section.
Describe the degree to which the incumbent is directly responsible for the management of funds. Indicate the variety of funding sources under the incumbent’s control:
Type of Budget
Current yr. expenditures $
Capital Budget \& Operating Budgets
$40M
*Total:*
*(To update total $, enter the $ amount in whole numbers (without the $ symbol \- e.g., 1,000,000\) then highlight the total sum (e.g., 1%) at the bottom of the column and press F9\.)*
$40 M
Supervision
Complete this section ONLY if the incumbent has direct or indirect supervision.
*Indicate job titles of employees supervised by this position, the number of positions and total headcount/number of positions, and total Full Time Equivalent (FTE).*
Payroll TitleDirect/Indirect
Total Headcount
Total
FTE
Data Scientists
Direct
4
3
Data Engineering
Direct
2
2
AI Medical Director
Direct
1
0\.5
Clinical Data Specialist
Direct
2
2
Program Manager
Direct
1
1
Technical Architects
Indirect
4
4
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At University of California - San Francisco, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. C-Level-level AI roles across all categories have a median of $250,000.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
University of California - San Francisco AI Hiring
University of California - San Francisco has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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