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About This Role
Overview
The Search & AI Visibility Lead develops and implements search marketing initiatives that support lead generation, occupancy, and revenue goals. Working collaboratively within the Performance Marketing team, this role executes and optimizes organic search and artificial intelligence (AI) visibility strategies across the Sun Communities and Sun Outdoors brands, including search engine optimization (SEO), generative engine optimization (GEO), technical and local search, content optimization, and search analytics. The Search & AI Visibility Lead partners with Brand & Creative, Web/Product, MarTech, Analytics, and agency partners to drive qualified traffic, leads, and bookings while establishing search best practices, performance benchmarks, and measurement frameworks that strengthen brand visibility and overall business performance across traditional search engines and emerging AI-powered discovery platforms.
Job Duties
- Collaborates with cross-functional teams and divisional leadership to develop and execute the Company's search and AI visibility strategy across the Sun Communities and Sun Outdoors brands. (Essential)
- Develops and manages SEO and GEO strategies at the corporate, regional, and property levels to increase organic visibility, drive qualified traffic, and support occupancy and revenue goals. (Essential)
- Builds standard operating procedures (SOPs), best practices, governance standards, and performance benchmarks for organic search and AI visibility programs. Tracks and reports on visibility, traffic, conversions, and return on investment (ROI). (Essential)
- Manages the implementation of search marketing initiatives within established timelines and assigned budgets. (Essential)
- Oversees technical SEO initiatives, including site health, crawlability, indexation, structured data, site performance, and other search optimization opportunities.
- Collaborates with Brand & Creative teams, content contributors, and agency partners to develop content strategies that improve search visibility, topical authority, and AI-driven discovery.
- Establishes and manages local search strategies, including business listings, location pages, reviews, and reputation management initiatives across the Company's portfolio.
- Develops key performance indicators (KPIs), dashboards, and reporting frameworks that measure search performance and provide actionable insights for marketing and business leaders.
- Provides search strategy, recommendations, and audience insights to support existing and new properties, marketing initiatives, and business objectives.
- Works in conjunction with digital marketing, media, and analytics teams to align search strategies with broader customer acquisition, engagement, and conversion initiatives.
- Coordinates agency and vendor relationships and monitors industry, search engine, and AI platform developments to identify opportunities for innovation and performance improvement.
- Acts as a player-coach, guiding agency partners, establishing best practices, and maintaining expertise in evolving search and AI search trends.
Requirements
- Bachelor's Degree in Marketing, Communications, Analytics, Business, or a related field (Required)
- 6 years in experience in SEO, organic search, or digital marketing (Required)
- Must have a valid driver's license
- Experience developing and executing search strategies that drive measurable business results
- Experience with AI search, GEO, and emerging search technologies
- Strong knowledge of technical SEO, content optimization, website architecture, and search performance best practices
- Experience with local search optimization, business listings, and reputation management programs
- Proficiency with Google Analytics, Google Search Console, and SEO platforms such as Semrush, Ahrefs, or similar tools
- Experience with reporting tools, dashboards, and performance measurement frameworks
- Excellent verbal and written communication skills
- Strong attention to detail, analytical, organizational, and project management skills
- Ability to manage multiple priorities and adapt to changing business needs
- Experience working with agency partners and cross-functional teams
- Experience in real estate, hospitality, travel, multi-location, or consumer services industries a plus
BENEFITS
At Sun Communities, you will be part of an industry-leading organization where you will be challenged, inspired, rewarded and transformed. We place a high priority on our team members, and this is a big part of what sets us apart. We will ask you to give us your very best every day, and will challenge you with interesting work, stretch assignments, a collaborative and supportive work environment and plenty of learning and growth. In exchange, we will reward you with great pay, advancement opportunities, paid time off, great benefits, and flexibility.
- Comprehensive Medical and Prescription coverage with 4 plan options so you can choose the plan that best meets the needs of you and your family
- Comprehensive Dental and Vision Plans
- On-Site Fitness Center
- Voluntary Health and Dependent Care Reimbursement Accounts
- Life, Accidental Death & Dismemberment Insurance and Dependent Life
- Short and Long-Term Disability Coverage
- 401(k) Plan with Sun matching contribution
- Employee Assistance Program
- Identity Theft Insurance
- Legal Assistance Plan
- Pet Insurance
- Tuition Reimbursement program providing financial support to team members who further their formal education
- Vacation RV Site Discounts for team members when visiting Sun resorts across the nation
- Team Member Perks & Discounts program with hundreds of discounts on things like travel, merchandise, mobile phone service, and more
- Up to six weeks of paid parental leave for the birth of a child, adoption, or placement of a child
- Paid Time Off including ten holidays, vacation, personal, sick time, bereavement and pay for jury duty
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Salary Context
This $120K-$140K 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 Sun Communities, 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. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($130K) sits 39% below the category median. Disclosed range: $120K to $140K.
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.
Sun Communities AI Hiring
Sun Communities has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Southfield, MI, US. Compensation range: $140K - $140K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 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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