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About This Role
Baptist Health is the region's largest not-for-profit healthcare organization, with 12 hospitals, over 29,000 employees, 4,500 physicians and 200 outpatient centers, urgent care facilities and physician practices across Miami-Dade, Monroe, Broward and Palm Beach counties. With internationally renowned centers of excellence in cancer, cardiovascular care, orthopedics and sports medicine, and neurosciences, Baptist Health is supported by philanthropy and driven by its faith-based mission of medical excellence. For 26 years, we've been named one of Fortune's 100 Best Companies to Work For, and in the 2025-2026 U.S. News & World Report Best Hospital Rankings, Baptist Health was the most awarded healthcare system in South Florida, earning 63 high-performing honors.
What truly sets us apart is our people. At Baptist Health, we create personal connections with our colleagues that go beyond the workplace, and we form meaningful relationships with patients and their families that extend beyond delivering care. Many of us have walked in our patients' shoes ourselves and that shared experience fuels our commitment to compassion and quality. Our culture is rooted in purpose, and every team member plays a part in making a positive impact – because when it comes to caring for people, we're all in.
At Baptist Health, we're committed to supporting our employees at every stage of their journey, both personally and professionally. Our approach is rooted in a "grow our own" philosophy, designed to help our team members build meaningful, long-term careers with us, supported by benefits that make a real difference, including:
- Career growth and development opportunities, with clear pathways and ongoing support
- Comprehensive health and wellness resources that go beyond traditional benefits
- A wellness program that can help employees eliminate their medical plan deductible, reducing out-of-pocket healthcare costs
- Tuition reimbursement to support continued learning and advancement
- And so much more
Together, these benefits and others reflect our commitment to caring for our people, so they can build fulfilling careers with us while making a meaningful impact every day.
Description:
The AVP of Imaging AI will report directly to the Corporate VP of Imaging serving as the clinical imaging AI executive for a large multi-hospital system including 25+ imaging diagnostic centers and 3 radiology practices and a radiology fellowship program. This role drives enterprise-wide strategy, governance, and adoption of AI solutions to eliminate duplication, overcome siloed implementation, and deliver measurable clinical, operational, and financial value. Key responsibilities include developing the AI roadmap aligned with enterprise imaging priorities (including PACS and Epic integrations); leading vendor evaluation, ROI analysis, negotiation, and contracting; collaborating with informatics on seamless integration and performance monitoring; spearheading change management, training, and adoption across practices; supporting the AI Governance Council; and partnering with Business Development and Marketing to educate physicians and patients on AI-enhanced capabilities as a market differentiator. The ideal candidate will combine strategic vision, technical acumen, change leadership, and deep healthcare domain expertise to operationalize responsible, high-impact AI that improves diagnostic accuracy, efficiency, patient outcomes, and competitive positioning.Qualifications:
Core Requirements:
- Master’s Degree required (e.g., Healthcare Informatics, Data Science, Business Administration, or a related technical/clinical field).
- Familiarity of healthcare regulatory, ethical, and compliance frameworks (FDA SaMD, HIPAA, ACR guidelines) is essential.
- Specific credentials, fellowships, or certified training in IT, Clinical AI Governance, Biomedical Informatics, or Imaging Informatics preferred to lead the Imaging AI Governance Council and drive peer-to-peer physician education.
- Demonstrated ability to translate complex AI concepts into actionable clinical and business strategies for executive, clinical, and technical audiences. Proven track record managing centralized technical vendor evaluations, clinical performance testing on local data, financial ROI modeling (focusing on efficiency and downstream revenue), contract negotiations (SLAs, data rights), and multi-year enterprise roadmaps. Strong understanding of PACS/RIS/EHR integrations (e.g., Epic, Sectra), technical change control, and continuous KPI monitoring (model drift, sensitivity, override rates). High emotional intelligence and political acumen required to quarterback an enterprise AI strategy and AI Governance Council and align cross-functional stakeholders.
If Clinician (non-MD/DO):
- Active professional licensure or registry in an allied health or nursing field (e.g., ARRT for Radiologic Technologists) is highly preferred.
- Imaging Informatics certification (e.g., CIIP via SIIM, PARCA) is strongly preferred.
- Project management (PMP) or formal change management certifications (e.g., Prosci) are highly advantageous to support multi-site PACS/RIS deployments, workflow integrations, and practice-level training.
- Minimum 5 years of leadership experience in radiology workflows and diagnostic imaging operations across a large multi-hospital or multi-practice ecosystem. Deep operational expertise in optimizing department operations and workflows. Proven success conducting enterprise-wide tool audits, proving efficacy and utility, eliminating duplicate applications, establishing lifecycle policies, running local validation pilots, and executing hands-on change management to drive standardized frontline clinician adoption. Strong strategic mindset to partner with Business Development and Marketing, serving as the clinical champion to educate community physicians and patients on AI-enhanced diagnostic capabilities as a market differentiator.
If Physician (MD/DO):
- Doctor of Medicine (MD) or Doctor of Osteopathic Medicine (DO) degree required.
- Board certification in Radiology or a related diagnostic specialty is preferred but not required.
- Specific credentials, fellowships, or certified training in Clinical AI Governance, Biomedical Informatics, or Imaging Informatics preferred to lead the Imaging AI Governance Council and drive peer-to-peer physician education.
- Minimum 5 years of clinical practice experience across a large multi-hospital or multi-practice ecosystem with a track record of administrative, quality assurance, or committee leadership within a multi-hospital system or radiology fellowship program. Proven peer-to-peer credibility to unify disparate radiology practices, establish medical consensus on clinical AI safety/human oversight, and manage clinical discrepancies. Proven success conducting enterprise-wide tool audits, proving efficacy and utility, eliminating duplicate applications, establishing lifecycle policies, running local validation pilots, and executing hands-on change management to drive standardized frontline clinician adoption. Strong strategic mindset to partner with Business Development and Marketing, serving as the medical champion to educate community physicians and patients on AI-enhanced diagnostic capabilities as a market differentiator.
EOE, including disability/vets
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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Baptist Health South Florida, 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 11,866 positions with disclosed compensation.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
Baptist Health South Florida AI Hiring
Baptist Health South Florida has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Miami, FL, US.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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