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About This Role
Job Function Summary:
Responsible for identifying and investigating new product opportunities within a defined range of responsibility. Develops project plans and manages the design, development, testing, and documentation of select products, services or enhancements. Formulates and executes business plans for product development and launch. Develops and participates in public outreach activities and informational campaigns to promote awareness of products or services. Maintains knowledge of new trends and products in the appropriate markets.
Generic Scope:
Technical leader with a high degree of knowledge in the overall field and recognized expertise in specific areas; problem-solving frequently requires analysis of unique issues / problems without precedent and / or structure. May manage programs that include formulating strategies and administering policies, processes, and resources; functions with a high degree of autonomy.
Custom Scope:
The Product Manager, Urology Bio-AI Nexus, reporting to the Vice Chair for Translational and Population Science Research and Associate Chair for Data and Analytics in the UCSF Department of Urology, is responsible for driving product integration, development coordination, and stakeholder alignment across the Nexus platform—a unified, multi-modal research and clinical data ecosystem supporting precision urology at UCSF. This role combines technical program management with direct product development support, operating at the interface between data engineering, clinical and translational research, and end users.
The Product Manager translates requirements from clinical and laboratory science faculty, and clinical research leadership, and other key staff into actionable specifications; coordinates development priorities and timelines; and contributes directly to the completion and documentation of software tools and data products. The position requires sufficient technical fluency to extend existing codebases using AI-assisted development tools, without requiring full-stack software architecture expertise.
The Product Manager operates with a high degree of autonomy, proactively identifying gaps between current platform capabilities and program needs, maintaining a living product roadmap, and ensuring that tools emerging from the Nexus meet standards of usability, reliability, and documentation required for use by clinical research staff, investigators, and institutional partners.
Department Overview:
The UCSF Department of Urology and UCSF Prostate Program curate one of the most comprehensive integrated data ecosystems in academic medicine, spanning electronic health records, natural-language-processed clinical reports, digital pathology, radiology imaging, biospecimens, and multi-omic data across more than 120 TB of on-premise research storage. The Bio-AI Nexus is the program’s strategic platform for connecting these resources into a coordinated, queryable, and scalable infrastructure that enables translational discovery, precision medicine, and AI model development.
The Nexus team is small, highly skilled, and fast-moving, with deep expertise distributed across clinical analytics, data architecture, biostatistics, and translational research. The Product Manager will serve as a critical integrating force—ensuring that the program’s technical assets are developed to production quality, that faculty and staff are aligned on priorities, and that the platform’s growing capabilities are accessible and well-documented for the broad community of faculty, trainees, and clinical research staff who depend on them.
Education
Req / Pref
Bachelor's degree in related area and / or equivalent experience / training. REQ
Advanced degree (MS, MPH, PhD, or MBA) in computer science, biomedical informatics, data science, public health, or a related field. Preferred
Knowledge, Skills and Abilities Req / Pref
Minimum 5 years of product management, technical program management, or equivalent experience in a data-intensive technology environment. Required
Demonstrated experience collecting user requirements, managing product backlogs, and driving software products from development to deployment. Required
Technical fluency sufficient to read, extend, and troubleshoot existing codebases (Python, SQL) using AI-assisted development tools; ability to contribute to incremental enhancements without full-stack development expertise. Required
Strong organizational and project management skills, including experience maintaining roadmaps and holding cross-functional teams to timelines and deliverables without formal supervisory authority. Required
Advanced oral and written communication skills; demonstrated ability to bridge technical and non-technical stakeholders. Required
Experience working in a research, clinical, or healthcare data environment. Preferred
Bachelor’s degree in a related field and/or equivalent experience/training. Required
Familiarity with electronic health record data structures (Epic/Clarity preferred) and clinical data standards (HL7, FHIR, OMOP). Preferred
Experience with data visualization, user interface prototyping, or exploratory data tools. Required
Familiarity with DICOM imaging data, digital pathology workflows, or AI/ML model deployment. Preferred
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 $118K-$185K range is in the lower quartile for AI Product Manager roles in our dataset (median: $185K across 149 roles with salary data).
View full AI Product Manager salary data →Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 10,827 AI roles we're tracking, AI Product Manager positions make up 4% of the market. At University of California - San Francisco, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $220,350 based on 994 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($151K) sits 31% below the category median. Disclosed range: $118K to $185K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,300; VP: $236,450.
University of California - San Francisco AI Hiring
University of California - San Francisco has 2 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $175K - $185K.
Location Context
AI roles in San Francisco pay a median of $260,600 across 2,767 tracked positions. That's 22% above the national median.
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 10,827 open positions tracked in our dataset. By seniority: 122 entry-level, 5,256 mid-level, 3,922 senior, and 1,527 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (665 positions). The remaining 10,097 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 10,827 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,288), AI Software Engineer (548), AI Product Manager (471). 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 (122) are outnumbered by mid-level (5,256) and senior (3,922) 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 1,527 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (665 positions), with 10,097 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 $213,480. Top-quartile roles start at $264,111, 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 $266,050 median, while Prompt Engineer roles sit at $140,000. 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 (2,093 postings), Aws (1,100 postings), Azure (910 postings), Rag (863 postings), Gcp (680 postings), Pytorch (549 postings), Claude (532 postings), Kubernetes (511 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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