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About This Role
Certain terms and conditions of employment for this position, including the rate of pay, benefits, etc., are currently subject to negotiation with the appropriate union.
Applies professional concepts and UCSF policies and procedures to increase successful researcher adoption of research data assets, computational environments, AI capabilities, and research technologies. Independently analyzes researcher needs and adoption barriers; identifies opportunities to apply AI and automation to streamline research processes, reduce manual effort, and improve researcher productivity; develops, tests, and operationalizes AI-enabled and automated workflow solutions within supported research environments; develops and maintains scalable onboarding, documentation, knowledge resources, workflow examples, and AI-assisted support capabilities. Validates researcher-facing workflows and recommends improvements to research services and user experience. Works across researchers, product management, data engineering, research infrastructure, and support teams to translate complex technical capabilities into practical guidance. Within defined procedures and practices, determines appropriate action and escalates product strategy, infrastructure priorities, architectural direction, service portfolio, funding priorities, and institutional communications decisions to responsible leadership.
Department Overview
UCSF Academic Research Services supports researchers in discovering, understanding, and effectively using institutional research data assets, computational environments, AI capabilities, and related research technologies. The ARS Research Data Services and Information Commons program works across research, product, data engineering, infrastructure, and support teams to improve researcher experience, increase successful self-service adoption, and develop scalable services and reusable institutional knowledge.
REQUIRED QUALIFICATIONS
- Bachelor's degree in a related area and/or equivalent experience/training.
- Strong knowledge of and at least 5 years of experience in data science principles, practices, and tools such as Python, R, SQL, and others.
- Broad knowledge of information technology concepts relevant to research data services, computational environments, AI-enabled capabilities, and user-facing research technologies.
- Demonstrated experience applying generative AI-enabled tools, or AI-assisted automation to practical data, analytical, or research workflows, including identifying appropriate use cases, developing or configuring workflow solutions, testing outputs, and evaluating effectiveness.
- Ability to analyze end-to-end research or data workflows and identify opportunities to use AI and automation to reduce manual effort, improve efficiency, increase reproducibility, or enable researcher self-service.
- Demonstrated ability to translate complex technical concepts, system capabilities, and workflows into clear, practical guidance for technical and non-technical users.
- Demonstrated experience authoring and maintaining technical documentation, onboarding materials, knowledge-base content, FAQs, workflow guides, or comparable user-enablement resources.
- Working knowledge of research data workflows and the design and use of data sources, analytical environments, or computational tools sufficient to validate end-to-end user workflows and troubleshoot moderately complex issues.
- Demonstrated working knowledge, skills and abilities associated with system problem identification and resolution. Knowledge necessary to design, set up, operate and correct malfunctions involving technology systems. Experience in incident response and digital forensics, including data collection, examination and analysis.
- Demonstrated testing and workflow-validation skills, including reproducing user-reported issues, validating documentation accuracy, and supporting acceptance testing for user-facing features.
- Familiarity with version-controlled documentation or code repositories and ability to create reusable technical examples, code snippets, sample notebooks, or reference workflows.
- Thorough problem-solving and process-analysis skills; ability to identify recurring user needs and adoption barriers, analyze feedback and usage patterns, and recommend scalable improvements.
- Familiarity with data quality, governance, privacy, security, and responsible data-use requirements in a research or comparable institutional environment.
- Interpersonal and communication skills sufficient to work effectively with researchers, engineers, product managers, program leadership, and support personnel at varying levels of technical expertise.
- Ability to work independently within defined procedures and practices, exercise sound judgment, manage multiple priorities, and meet deadlines while contributing effectively as part of a cross-functional team.
PREFERRED QUALIFICATIONS
- Experience developing AI-ready knowledge resources, AI-assisted support capabilities, agentic or automated workflows, retrieval-augmented applications, or comparable AI-enabled solutions.
- Broad knowledge of databases and data systems. Knowledge relating to complex database system design. In-depth database querying and modeling skills working with moderately complex databases. Knowledge of conceptual database design. Skills necessary to develop procedures and standards relating to database management systems. Skills associated with implementing database enhancements that improve performance and reliability of the system at the software, hardware and operating system level.
- Experience supporting academic, biomedical, clinical, or other research communities and familiarity with researcher-centered service design or adoption practices.
- Experience delivering onboarding sessions, office hours, technical presentations, or community-based user enablement.
- Experience contributing to scoped research data, analytical, or consulting engagements and converting lessons learned into reusable organizational resources.
About UCSF
The University of California, San Francisco (UCSF) is a leading university dedicated to promoting health worldwide through advanced biomedical research, graduate-level education in the life sciences and health professions, and excellence in patient care. It is the only campus in the 10-campus UC system dedicated exclusively to the health sciences. We bring together the world’s leading experts in nearly every area of health. We are home to five Nobel laureates who have advanced the understanding of cancer, neurodegenerative diseases, aging and stem cells.
Pride Values
UCSF is a diverse community made of people with many skills and talents. We seek candidates whose work experience or community service has prepared them to contribute to our commitment to professionalism, respect, integrity, diversity and excellence – also known as our PRIDE values .
In addition to our PRIDE values, UCSF is committed to equity – both in how we deliver care as well as our workforce. We are committed to building a broadly diverse community, nurturing a culture that is welcoming and supportive, and engaging diverse ideas for the provision of culturally competent education, discovery, and patient care. Additional information about UCSF is available here .
Join us to find a rewarding career contributing to improving healthcare worldwide.
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 within this position classification at UCSF. For positions that are represented by a labor union, placement within the salary range will be guided by the rules in the collective bargaining agreement.
To learn more about the benefits of working at UCSF, including total compensation, please visit: https://ucnet.universityofcalifornia.edu/compensation-and-benefits/index.html
Salary Context
This $99K-$175K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 Required
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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($137K) sits 35% below the category median. Disclosed range: $99K to $175K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
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. Compensation range: $175K - $175K.
Location Context
AI roles in San Francisco pay a median of $261,000 across 2,646 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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