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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 digital content, search administration, SEO, paid search, and AI visibility. + Own the vendor and platform portfolio across enterprise SEO, rank tracking, local listings, and AI visibility, and advise on paid search management platforms, partnering with IT and Procurement on selection, consolidation, and renewal. + Lead AI adoption and capability development within Marketing and Communications, aligned with enterprise AI platforms, standards, and guidance, including curriculum, prompt library, and a tiered path from first use to power user, measured by adoption and time saved rather than attendance. + Serve as Marketing's primary liaison to University IT on matters affecting search, discoverability, and AI visibility. + Hold this work to the institutional standards a regulated environment requires, including HIPAA-aware handling of marketing data, tracking technology practices that distinguish authenticated from unauthenticated experiences, WCAG 2.1 AA conformance, and human review before AI-assisted content reaches a patient-facing surface. + Serve as Marketing's voice on the search and answer engine landscape, briefing senior leadership and turning a fast-moving technical field into clear investment decisions.
- HOW THIS ROLE IS MEASURED + Blended search economics: a single, trusted view of total qualified demand and cost per acquired patient, applicant, and referral across organic and paid together, not each in isolation. + Paid search quality and advisory impact: adoption of enterprise paid search standards, campaign conformance at review, and documented cases where advisory input changed allocation or improved efficiency. + Organic growth: non-brand entrances, conversions, and rank share against a defined keyword set by service line and academic program. + Technical health of the estate: indexation coverage, rendering and crawl defects, Core Web Vitals, and no unmanaged traffic loss through migrations, redesigns, or platform moves. + Provider and location discovery: profile completeness, directory accuracy, review volume, appointment starts, and call clicks from discovery surfaces. + AI visibility: a published institutional baseline for citation and recommendation share across the major answer engines, measured growth against a named set of peer academic medical centers, and AI crawler access to priority properties, verified with University IT.Practice maturity: enablement adoption and time saved, and a documented experiment log of what was tested, scaled, and retired.
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CORE QUALIFICATIONS
- Bachelor's degree in Marketing, Business, Communications, Digital Strategy, or a related field.
- Ten or more years in search marketing spanning both SEO and paid search, including four or more years leading teams, functions, or enterprise-scale programs.
- Hands-on command of paid search at scale, including experience guiding a meaningful annual search media budget and a clear point of view on automated query matching, Performance
- Max, and generated assets.
- Deep technical SEO expertise across large, complex digital ecosystems, plus keyword and content strategy depth and local and multi-location search including Google Business Profile at scale.
- Demonstrated work on AI search visibility, including prompt-level tracking and citation measurement, and a working understanding of how large language models retrieve and ground answers, sufficient to make sound calls on content structure, entity data, and crawler access without relying on vendor narratives.
- Fluency with the measurement stack: Search Console, GA4 and tag management, enterprise SEO and rank tracking platforms, server log analysis, paid platform reporting, and attribution models and their limits.
- Evidence of running search as a portfolio rather than a set of channels, including allocation recommendations you made and can defend.
Experience building a capability or team inside a large, complex organization, with the executive communication range to make a fast-moving technical topic decision-ready and the experimentation discipline to design tests that can fail. *
PREFERRED
- Healthcare, academic medicine, higher education, or another complex, highly regulated industry, ideally multi-location or multi-brand where enterprise and local search have to work together.
- Familiarity with HIPAA constraints on marketing technology and paid media targeting, web accessibility obligations, and responsible AI governance frameworks.
- Hands-on experience with an enterprise SEO platform such as Semrush, Conductor, Botify, or BrightEdge.
- Hands-on experience with an AI visibility platform such as Profound, Evertune, Scrunch, Otterly.AI, Peec AI, or Siteimprove.
- Working knowledge of enterprise search and content discovery platforms and their impact on audience engagement and content findability.
- Paid search management through a bid and portfolio platform such as Skai or Marin, and experience with a modern composable or headless content platform.
- Bilingual discoverability in English and Spanish, and public thought leadership that helps attract talent to a new function.
The University of Miami offers competitive salaries and a comprehensive benefits package including medical, dental, tuition remission and more.
UHealth-University of Miami Health System, South Florida's only university-based health system, provides leading-edge patient care powered by the ground breaking research and medical education at the Miller School of Medicine. As an academic medical center, we are proud to serve South Florida, Latin America and the Caribbean. Our physicians represent more than 100 specialties and sub-specialties, and have more than one million patient encounters each year. Our tradition of excellence has earned worldwide recognition for outstanding teaching, research and patient care. We're the challenge you've been looking for.
The University of Miami is an Equal Opportunity Employer. Applicants and employees are protected from discrimination based on certain categories protected by Federal law.
Job Status:
Full time Employee Type:
Staff
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At University of Miami, 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 $212,000 based on 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
University of Miami AI Hiring
University of Miami has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Coral Gables, FL, US, Miami, FL, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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