Director, Search & AI Visibility

Current Employees: If you are a current Staff, Faculty or Temporary employee at the University of Miami, please click here to log in to Workday to use the internal application process. To learn how to apply for a faculty or staff position, please review this tip sheet . The Department of Marketing Technology is currently seeking a full-time Director, Search & AI Visibility to work on site in Miami. In this capacity, the incumbent will search drives for new patients, applicants, and referrals than any other channel we have. Paid and organic searches still produce most of the qualified demand for UHealth, the Miller School of Medicine, and the Academy. What's changed is what sits above the results. Clinical and academic queries increasingly return an AI-generated answer first, and only the institutions named in that answer stay in consideration. This role runs searches as one practice across paid, organic, and AI answers. Those three are now connected in practical ways. Paid query data shows what content to build. Content strong enough to rank organically is the content answer engines cite. Where AI answers cut into organic clicks, paid has to cover the gap. Run separately, they compete for budget instead of informing it. You will own the technical and content foundation for organic, the measurement system for AI visibility, and the enterprise standards and advisory function for paid search, reported on a single scorecard. Scope covers Google and Bing, Google Ads and Microsoft Advertising, AI Overviews and AI Mode, ChatGPT, Gemini, Perplexity, and Copilot. Because no one in academic medicine has yet connected AI visibility to patient acquisition with any rigor, you will build that baseline and the model behind it. CORE RESPONSIBILITIES:

• Integrated Search Strategy
• Bring paid, organic, and AI visibility into one view of search economics, and advise marketing strategists on where the next dollar should go.
• Build the demand model that says what a dollar moved between brand and non-brand, between service lines, or between paid and organic is expected to return, and reconcile forecast to actual every cycle so allocation advice is grounded in measured results.
• Read the three channels against each other. Use paid query data to find the demand organic and AEO should chase, use organic and citation performance to decide where paid is buying coverage we already own, and manage cannibalization deliberately rather than discovering it in a quarterly report.
• Identify where AI answers are displacing organic clicks on priority terms and adjust the paid and content response before the traffic loss shows up in a ranking tool.
• Own keyword and content strategy across the patient, student, referring physician, and researcher journeys, built so a single asset can rank, convert paid traffic, and be cited in an answer rather than requiring three versions of the same page.
• Own conversion rather than clicks, including landing page testing, appointment request and application flows, call tracking, and the handoffs into scheduling and CRM.
• Publish one search scorecard segmented by service line and academic program, built on Search Console, GA4 and tag management, rank and share of voice tracking, server log analysis, paid platform reporting, and attribution that survives scrutiny, with a live read on which peer academic medical centers are winning which terms.
• Paid Search Advisory
• Serve as the in-house paid search expert for marketing strategists across UHealth, the Miller School, and the Academy, advising on campaign architecture, keyword and audience strategy, bid strategy, and budget allocation across service lines and academic programs.
• Advise on the balance between paid and organic investment, identifying where we are paying for terms we already win organically, where organic gaps justify paid coverage, and where AI answers have changed the value of a click.
• Set enterprise paid search standards and review campaigns against them, covering account structure, query hygiene, negative keyword strategy, conversion tracking, and landing page quality.
• Provide a tested point of view on automated query matching, Performance Max, and generated assets, including where automation earns its keep and where it spends against demand we already own.
• Review agency and media partner recommendations on behalf of marketing strategists, and evaluate performance claims against measured incrementality rather than platform-reported conversions.
• Equip strategists to make their own decisions well, through training, documented standards, and shared reporting rather than case-by-case approvals.
• Organic Search
• Own technical SEO requirements, including crawlability and indexation, rendering, site architecture, internal linking, page experience, canonicalization, hreflang for English and Spanish, and structured data, partnering with University IT and platform teams on implementation.
• Write SEO requirements into migrations, redesigns, and platform moves as acceptance criteria in the delivery backlog, and work with delivery partners to verify them before release, so traffic performance is protected through the change rather than assessed after it.
• Raise the clinical and academic authority signals that ranking systems reward, including named authors and medical reviewers, credentials, cited evidence, review dates, and clear editorial provenance.
• Protect and grow provider and location discovery through local SEO, Google Business Profile at scale, accuracy of provider and location data on discovery surfaces drawn from the enterprise provider source of truth, and review volume and response, recognizing that AI Mode surfaces a materially different set of practices than the local pack and the same assets have to win in both.
• Make writers and subject matter experts productive inside your standards, so quality scales without a bottleneck at your desk.
• Answer Engine Optimization
• Build out the AI visibility measurement system: a governed panel of high-intent prompts, citation and recommendation share by engine, sentiment and framing, and AI referral attribution, partnering with University IT to incorporate technical crawl and access insights into business-facing AI visibility and discoverability reporting.
• Track AI Overviews, AI Mode, and assistant answers as separate surfaces, because the sources they cite largely do not overlap and a single blended number will mislead the enterprise.
• Make our content retrievable by systems that retrieve passages rather than pages, do not execute JavaScript, and read PDFs poorly, defining discoverability requirements for passage-level structure, answer-first formatting, and clean semantic HTML, and working with University IT and platform teams to ensure solutions support search and AI visibility objectives.
• Own entity strategy for discovery, meaning one consistent machine-readable representation of the institution, its hospitals, service lines, physicians, faculty, and programs across our properties, aligned with the authoritative records in NPPES, ORCID, PubMed, and Wikidata and the third-party directories these systems cross-check.
• Partner with University IT and Information Security to communicate marketing visibility requirements and evaluate the impact of crawler-access and bot-management decisions on discoverability, audience reach, referral traffic, and AI answer-engine visibility.
• Own the workflow for correcting answer engines that misstate our physicians, services, locations, or outcomes, working with clinical, brand, and compliance partners.
• Hold vendor claims and popular AEO tactics to the same evidence standard as everything else, run experiments with stated hypotheses and holdouts where feasible, retire what does not work, and publish internal findings so the organization learns faster than the market does.
• Team, Enablement, and Standards
• Lead, hire, and develop a team spanning d