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About This Role
The Marketing AI & Automation Manager analyzes and improves business processes across SecureSpace Self Storage by designing, implementing, and governing AI-enabled workflows and integrated systems. This individual-contributor role has no direct reports and works under general supervision. The role evaluates operational needs and exercises independent judgment in selecting technical approaches, establishing workflow controls, and resolving complex process and integration issues within approved platforms, policies, and budgets.
Objective:
Advise marketing leadership and business owners on automation opportunities that improve operating efficiency, data quality, and decision-making. Analyze needs, define requirements, compare alternatives, and recommend priorities based on business impact, cost, feasibility, and risk. The role owns solution design and delivery for workflows used across teams, including content operations, reputation management, campaign tracking, reporting, and internal processes. Marketing leadership retains strategy, budget approval, and final business-priority decisions; the manager determines how approved objectives are achieved without step-by-step direction.
Responsibilities/Essential Functions:
- Evaluates cross-functional processes, identifies root causes of inefficiency, and recommends whether to redesign, automate, retain, or discontinue a workflow based on operational impact, risk, and cost.
- Defines business and technical requirements with stakeholders; independently selects workflow architecture, data structures, integration methods, and tools within approved platforms and budgets. Recommends new platforms or expenditures with documented trade-offs for approval.
- Designs and implements content-production systems with the SEO Manager and other business owners, determining workflow stages, validation rules, human-review controls, publishing safeguards, and performance measures for briefs, drafting, auditing, and approved distribution.
- Analyzes reputation-management needs and designs review-intake, categorization, escalation, response-approval, and follow-up systems; recommends controls that improve responsiveness while preserving compliant human decision-making.
- Evaluates and redesigns internal processes such as print-material requests, purchasing approvals, fulfillment handoffs, task routing, and recurring reporting; determines automation logic and exception controls within established business policies.
- Advises campaign and partnership owners on scalable execution and measurement approaches; designs ownership, handoff, tracking, and reporting frameworks for approved marketing initiatives without assuming ownership of marketing strategy.
- Determines alert logic and investigation criteria for ranking changes, lead trends, reputation issues, and system failures; evaluates false positives and business impact and recommends corrective priorities to the responsible business owners.
- Designs and implements integrations among CRM, call-tracking, content, reporting, and project-management systems; establishes field mappings, data-validation rules, location identifiers, campaign tags, and reconciliation controls.
- Analyzes data quality and measurement gaps affecting calls, inquiries, reservations, move-ins, and revenue reporting; recommends improvements and explains attribution limitations to support informed business decisions.
- Owns testing, rollout, monitoring, and ongoing improvement of new and existing AI and automation systems within the role's scope. Independently diagnoses failures, determines recovery and corrective actions, and pauses or rolls back unsafe workflows within established incident procedures; escalates material security, compliance, budget, or business-policy issues.
- Establishes solution-level access, credential, logging, and human-approval controls consistent with company policy; evaluates implementation risks and recommends safeguards before deployment. Business owners retain approval of publishing, spending, customer responses, and other sensitive actions.
- Maintains a prioritized automation roadmap for leadership review, documents design decisions and operating procedures, trains users, and evaluates adoption, net time savings, reliability, turnaround time, and ongoing costs. Recommends enhancements or retirement of systems that do not deliver sufficient value.
- Establishes and applies workflow-design, documentation, testing, and change-management standards for AI and automation systems within the role's scope, consistent with company IT policies and leadership-approved standards. Reviews requests and changes affecting those systems before deployment.
Compliance Requirements:
- Use only company-approved automation platforms, AI tools, and integrations; process customer and employee data only in approved systems and in accordance with company IT, information security, and data privacy policies and applicable privacy laws.
- Store credentials in approved secure systems only; do not use personal accounts, share credentials, or embed passwords, API keys, or tokens in workflows or documents.
- Do not create, write, solicit, or post reviews on behalf of customers or employees, and do not suppress, filter, or selectively request reviews based on expected sentiment, consistent with the FTC's rules on consumer reviews and testimonials.
- Ensure automated customer communications (including text messages, calls, and emails) comply with applicable consent, opt-out, and communication laws.
- Clearly identify AI-generated content for human review before any publishing, customer response, or other external use.
Other Functions:
- Plans deployment and maintenance activities with affected teams and determines the technical sequence, testing approach, and recovery procedures within approved project objectives.
- Evaluates emerging AI and automation capabilities and advises leadership on practical adoption based on comparative benefits, cost, reliability, and risk.
- Other duties as assigned.
Skills and Experience Required:
- Demonstrated experience independently analyzing business processes and designing, deploying, and maintaining live automation systems in marketing operations, business operations, technology, or a related setting.
- Hands-on proficiency with at least one workflow platform such as n8n, Make, or Zapier, including branching logic, schedules, retries, and error handling.
- Experience using generative AI tools, model APIs, or AI agents within repeatable workflows, including structured outputs, quality checks, human review, and cost controls.
- Hands-on experience integrating REST APIs and webhooks, including authentication methods (API keys, OAuth), secure credential handling, JSON payloads, field mapping, rate limits, and debugging failed requests.
- Working knowledge of a scripting language, such as JavaScript or Python (Google Apps Script a plus), to build custom logic, transform data, and extend integrations beyond no-code tools.
- Strong spreadsheet and data-validation skills, with the ability to reconcile metrics across systems and explain data-quality or reporting limitations.
- Strong analytical, problem-solving, organizational, and time-management skills, with demonstrated ability to define ambiguous requirements, evaluate alternatives, and make sound technical and process decisions under general supervision.
- Clear written and verbal communication skills, including documenting processes and training nontechnical users.
- Ability to advise nontechnical stakeholders, recommend priorities based on business impact, and exercise independent judgment within approved policies and budgets while handling confidential information responsibly.
- Experience with CRM or CMS systems, reputation-management tools, Google Workspace, Wrike, dashboards, SQL, or version control (Git) is preferred. Familiarity with SEO and digital marketing concepts is helpful, but this role is primarily technical.
Additional Requirements:
- Occasional work outside normal business hours may be needed to resolve critical workflow failures; routine after-hours coverage is not expected.
- Maintains documented escalation and manual-fallback procedures so issues arising outside business hours can be handled without disruption.
Physical Requirements:
- Prolonged periods of working at a computer and using digital systems.
- Ability to perform the essential functions of the position with or without reasonable accommodation.
Salary Range:
- $75,000 - $80,000
Ares Storage Management LLC is committed to providing equal employment opportunities to all employees and applicants. We strictly prohibit discrimination and harassment of any kind based on race, creed, color, religion, age, sex, national origin, disability, genetic information, sexual orientation, gender identity or expression, marital status, citizenship or immigration status, honorably discharged veteran or military status, or the presence of any sensory, mental, or physical disability, or the use of a trained dog guide or service animal by a person with a disability, or any other characteristic protected by federal, state, or local laws.
This policy applies to all employment practices, including but not limited to recruitment, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.
Ares Storage Management LLC complies with all applicable disability laws and is committed to providing reasonable accommodation to qualified individuals with disabilities. If you need a reasonable accommodation to participate in the application or interview process, perform essential job functions, or access other employment-related benefits, please contact Human Resources at hr @ insitepg.com.
Salary Context
This $75K-$80K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At SecureSpace Management, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($77K) sits 63% below the category median. Disclosed range: $75K to $80K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
SecureSpace Management AI Hiring
SecureSpace Management has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $80K - $80K.
Remote Work Context
Remote AI roles pay a median of $188,200 across 2,430 positions. About 6% of all AI roles offer remote work.
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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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