Interested in this AI/ML Engineer role at Extra Space Storage?
Apply Now →About This Role
*\*\*\* Please note that this is a HYBRID role, requiring this person to work at our Cottonwood Heights office in SLC at least 3 days per week \*\*\**
It is an exciting time to be at Extra Space. We have been featured on multiple "Best Places to Work" awards, including Forbes and Glassdoor. We’re an S\&P 500 company that hasn’t stopped growing since our founding in 1977\. Today we are leading the self\-storage industry in more ways than one, but in order to maintain this lead, we need exceptionally motivated, capable, and driven people like you. Self\-storage is our product, helping people is our passion. Come grow with us and find out why so many of our employees recommend us as a great place to work.
The Director of SEO \& AI Search is responsible for leading the company’s entire Local \& Organic SEO ecosystem across 4,000\+ self\-storage facilities and all core digital properties. This role helps set the strategic vision, roadmap, and executional strategy for enterprise\-level organic visibility, including Local SEO, Technical SEO, Content SEO, on\-site optimization, and AI\-driven search. The Director will oversee a high\-performing SEO team, and maintain a strong relationship with agency partners, ensuring alignment, collaboration, and strong execution across every stage of the funnel. This leader will play a key role in positioning Extra Space at the forefront of the rapidly evolving search landscape experiences that transform how customers discover self\-storage.
Primary Responsibilities:
- Lead and elevate the company’s full Local \& Organic SEO strategy with a focus on visibility growth, scalable execution, and long\-term competitive advantage.
- Oversee Local SEO and Organic SEO, ensuring unified prioritization, strong communication, and seamless collaboration with Web Optimization, Social Media, Paid Search, Data\-Science, and IT.
- Develop and manage a comprehensive roadmap that supports both day\-to\-day performance and future\-focused initiatives tied to AI search experiences.
- Measure and optimize organic performance, reporting regularly on KPIs, trends, testing outcomes, technical improvements, opportunities for growth.
- Provide strategic direction for AI search visibility, including AI Overviews optimization, schema expansion, and semantic SEO frameworks to ensure Extra Space is well\-positioned for the next generation of search platforms.
- Identify, test, and evaluate new SEO technologies, methodologies, and innovations that improve efficiency and strengthen the overall organic strategy.
- Work closely with SEO managers to prioritize and guide development efforts with SEO developers to address technical SEO needs, including crawlability, indexation, internal linking, site architecture, Core Web Vitals, user experience, and content performance.
- Ensure SEO strategies fully reflect core marketing principles, CRO best practices, and a deep understanding of user behavior across both local search and traditional organic search journeys.
- Report out regularly on SEO KPIs and be accountable for driving sustained improvements across these indicators.
- Provide timely performance insights, trend analysis, testing outcomes, and visibility narratives to leadership, along with clear strategic recommendations and action plans.
- Manage SEO budgets, resource planning, vendor partnerships, and agency relationships to support a scalable and efficient execution model.
- Analyze performance data to uncover new opportunities and provide high\-level recommendations that support acquisition goals.
- Mentor and develop SEO team members, providing clear guidance, feedback, growth plans, and ongoing support as they build expertise in Local Search, Technical SEO, Content SEO, and AI search innovation.
- Ensure Extra Space remains a leader in organic search visibility and is well\-positioned for the future of AI\-driven discoverability.
Job Specifications
- Manage team and inspire them with forward thinking vision.
- Grow Extra Space Storage’s online presence with integrity and using only best practices.
- Work closely with other digital marketing teams collaborating and creating a broad approach to new customer acquisition.
- You must be authentic and collaborative with general excitement to tell the story of your wins and losses to leadership and coworkers.
- Keen eye for other teams/departments projects that may have an unexpected impact on SEO.
- Responsible for being a problem solver that utilizes data to support direction and thrives on driving innovation.
- Ability to translate complex data into actionable plans for company leaders.
Education and Experience:
- Degree in Marketing, Advertising, IT or related field
- 8\+ years in SEO or related position either with an agency or brand
- Experience developing websites, HTML, and standard mark\-up preferred
- Strong knowledge and experience with AI platforms
- Strong experience in SEO testing, including designing hypotheses, managing controlled experiments, interpreting test results, and translating findings into scalable recommendations.
- Proven ability to build tools and internal applications that improve workflow efficiency, automate repetitive tasks, streamline data analysis, or enhance cross\-team visibility into SEO performance.
- Experience with video as a growth channel, including optimizing video content for search, understanding YouTube/Google discoverability, and leveraging video as part of a broader organic visibility strategy.
Some of our great Benefits include:
- Competitive compensation package
- Medical, dental, vision and company paid life insurance and AD\&D
- 11 paid holidays and generous PTO
- Generous 401k match
- HSA \& FSA accounts
- Pet insurance
- Discounted cell phone plan provider
If you are a current Extra Space employee, please apply through Jobs Hub in Workday.
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We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
Applications Deadline: Applications will be accepted until the position is filled.
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Extra Space Storage, 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 in Demand for This Role
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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Extra Space Storage AI Hiring
Extra Space Storage has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Salt Lake City, UT, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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