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About This Role
Job Description – SEO Manager, AI Search & GEO
Division / Department: Marketing
Reports To (Position): Director of Organic Search
Level / Grade: Manager
Location: Remote (US)
Employment Type: Full-Time, Exempt
Work Conditions: Remote Office Environment
Travel: Minimal, as needed
Telecommute: Yes
Minimum Educational Requirement: Bachelor's degree in Marketing, Business, Communications, Analytics, Information Technology, or a related field; equivalent experience considered
Years of Experience: 5+ years of hands-on SEO experience across technical SEO, content optimization, keyword research, and organic performance analysis
Position Summary:
Career Certified is a multi-brand professional licensing education platform serving regulated industries, including real estate, financial services, insurance, and AEC. Our nine brands collectively serve hundreds of thousands of learners annually across pre-licensing, exam prep, continuing education, and compliance training.
Google remains one of the most important ways prospective students find our brands, courses, and educational content. At the same time, platforms such as ChatGPT, Google AI Overviews and AI Mode, Gemini, Perplexity, and Claude are becoming another layer of search and discovery that are branching off traditional SEO. We are looking for an experienced SEO practitioner who can help us grow across both.
The SEO Manager, AI Search & GEO will be a core member of the SEO team and responsible for helping with hands-on technical SEO, on-page optimization, content strategy, competitive research, and organic performance across a portfolio of Career Certified brands. The role will also help build our approach to Generative Engine Optimization (GEO), Answer Engine Optimization (AEO), and AI search visibility.
This is not an AI-only role, and it is not a strategy-only role. You should be comfortable diagnosing a technical SEO issue, building a keyword and content strategy, reviewing schema markup, analyzing Search Console data, and then applying those same skills to understand why one brand is appearing in AI-generated answers while another is not.
The role reports to the Director of Organic Search and works closely with content, web, ecommerce, analytics, and brand marketing teams.
Essential Functions / Job Responsibilities:
Technical SEO
- Monitor and improve the technical SEO health of assigned Career Certified websites, including crawling, indexation, canonicalization, redirects, XML sitemaps, robots directives, and site performance.
- Identify technical issues that may limit search engine discovery, crawling, rendering, or indexing and work with web teams to resolve them.
- Implement and maintain structured data.
- Review site architecture, URL structure, navigation, and internal linking to improve crawlability, topical relationships, and organic visibility.
- Support website migrations, redesigns, CMS changes, and new site or brand launches with SEO requirements, QA, and post-launch monitoring.
- Diagnose unexpected traffic, ranking, or indexing changes and identify likely causes.
SEO Strategy & Execution
- Conduct keyword research and search intent analysis across Career Certified's industries, products, states, license types, and customer journeys.
- Build and maintain keyword and topic maps that connect search demand to the right pages and content types.
- Identify organic growth opportunities across commercial, category, product, educational, and informational searches.
- Optimize title tags, meta descriptions, headings, page copy, internal linking, and other on-page elements.
- Evaluate existing content and determine where pages should be updated, consolidated, expanded, repositioned, or retired.
- Conduct competitor research using rankings, content, backlinks, SERP features, and topic coverage to identify gaps and opportunities.
- Help grow brand authority through digital PR, partnerships, link acquisition, and content that naturally earns citations and references.
- Monitor changes in Google Search and translate those changes into practical recommendations for Career Certified brands.
GEO, AEO & AI Search
- Develop and continually refine Career Certified's approach to Generative Engine Optimization and Answer Engine Optimization.
- Identify the questions, prompts, topics, and recommendation queries where our brands should be visible across ChatGPT, Google AI Overviews and AI Mode, Gemini, Perplexity, Claude, and other emerging search experiences.
- Analyze which sources, brands, pages, and content formats AI systems are citing for important topics in our categories.
- Determine why competitors are being cited where Career Certified brands are not and translate those findings into specific SEO, content, authority, or technical actions.
- Apply traditional SEO concepts such as topical authority, internal linking, entity clarity, structured data, source authority, and content quality to improve visibility across both traditional and AI-driven search.
- Evaluate content for characteristics that make it easier for search engines and AI systems to understand and reference, including clear answers, factual support, semantic completeness, strong page structure, and original expertise.
- Test GEO and AEO tactics and document what produces measurable changes in visibility, citation frequency, referral traffic, or downstream performance.
- Stay current on how AI search platforms discover, retrieve, summarize, and cite web content without chasing tactics that cannot be validated.
- Develop automation processes and assist with internal education.
AI Search Measurement
- Build and maintain a consistent set of priority prompts and topics for each brand.
- Track brand mentions, citations, source inclusion, competitive share of voice, and changes in AI search visibility over time.
- Evaluate AI search visibility by platform, brand, topic, and stage of the customer journey.
- Use tools such as Profound, Semrush, Ahrefs, Athena, or other emerging platforms where appropriate, while recognizing the limitations of current AI visibility measurement.
- Combine AI visibility data with GA4, Google Search Console, referral traffic, branded search trends, and other signals to understand business impact.
- Develop reporting that helps leadership understand where AI search is creating visibility, where competitors have an advantage, and where we have an opportunity to improve.
Content Strategy & Optimization
- Work with content teams to develop pages that are built around real search demand and user questions rather than content volume alone.
- Create content briefs that incorporate keyword research, search intent, competitive analysis, related questions, topical coverage, and AI citation opportunities.
- Improve existing pages so they provide clearer, more complete, and more authoritative answers than competing content.
- Identify opportunities to incorporate original expertise, proprietary data, subject matter experts, examples, research, tools, and other first-party information that competitors cannot easily replicate.
- Develop scalable standards for FAQs, educational content, licensing guides, product pages, comparison content, and other recurring page types.
- Action opportunities for improving sourcing, expert involvement, authorship, accuracy, transparency, and first-hand knowledge where appropriate.
Reporting & Analysis
- Monitor organic search performance using Google Search Console, GA4, Ahrefs, Semrush, and other SEO platforms.
- Track rankings, clicks, impressions, CTR, conversions, revenue, and other performance indicators based on the purpose of each page and brand.
- Identify meaningful changes in organic performance and explain what is driving them.
- Build reporting that connects SEO activity to business outcomes rather than rankings alone.
- Surface emerging opportunities, competitive threats, and search behavior changes to the Director of Organic Search and other stakeholders.
Cross-Functional Collaboration
- Partner with web and ecommerce teams to make sure SEO requirements are incorporated into development work before changes go live.
- Work with content and brand marketing teams to align editorial plans with organic search opportunities, licensing cycles, regulatory changes, and enrollment periods.
- Translate SEO and AI search findings into clear recommendations for teams that do not work in search every day.
- Present organic and AI search performance, opportunities, and risks to leadership in straightforward business language.
- Perform other duties as assigned.
What Success Looks Like:
- Career Certified brands improve visibility across both traditional organic search and emerging AI-driven search experiences.
- Technical SEO issues are proactively identified, prioritized, and resolved.
- SEO and content strategies are grounded in measurable search demand, customer intent, and competitive intelligence.
- Career Certified brands increase their presence and citations within relevant AI-generated answers and recommendation queries.
- GEO and AEO initiatives are systematically tested and evaluated based on measurable results.
- Organic search initiatives demonstrate a clear connection to traffic, conversions, revenue, and other meaningful business outcomes.
- Leadership receives clear reporting on traditional organic search performance, AI search visibility, competitive threats, and growth opportunities.
- Content, web, ecommerce, analytics, and brand teams receive clear and actionable recommendations that improve organic performance.
Minimum Requirements:
- 5+ years of hands-on SEO experience across technical SEO, content optimization, keyword research, and organic performance analysis.
- Demonstrated experience diagnosing and resolving technical SEO issues rather than simply identifying them.
- Strong understanding of how search engines crawl, render, index, rank, and surface web content.
- Experience building SEO strategies across large websites, multiple brands, multiple clients, or complex product portfolios.
- Experience using data from Google Search Console and GA4 to identify problems and opportunities.
- Hands-on experience with Ahrefs, Semrush, Screaming Frog, Sitebulb, or similar SEO platforms.
- Practical experience testing GEO, AEO, LLM optimization, or AI search visibility strategies.
- Ability to explain what you have tested in AI search, what you measured, and what you learned.
- Comfortable working in an area where best practices are still developing and many industry claims have not been proven.
- Comfortable owning execution on a lean team without requiring a large support structure.
- Able to manage multiple brands and priorities at the same time.
- Strong written and verbal communication skills, including the ability to explain technical concepts to non-technical stakeholders.
- Familiarity with exam cycles, regulatory seasonality, or state-specific licensing content preferred.
- Experience and comfort with BigQuery, Looker Studio/Data Studio, SQL, and log file analysis preferred.
- High level of comfort working with large SEO datasets preferred.
Skills Required:
- Strong analytical, problem-solving, and critical-thinking skills.
- Strong understanding of technical SEO and organic search strategy.
- Advanced keyword research and search intent analysis skills.
- Strong content strategy and on-page optimization capabilities.
- Ability to translate complex SEO and AI search concepts into clear business recommendations.
- Strong competitive analysis and research skills.
- Ability to identify and prioritize organic growth opportunities.
- Strong written and verbal communication skills.
- Ability to collaborate effectively across content, web, ecommerce, analytics, and marketing teams.
- Ability to manage multiple brands and competing priorities in a fast-paced environment.
- Detail-oriented with a high level of accountability and ownership.
Technical Skills Required:
- Technical SEO and crawl diagnostics.
- Google Search Console.
- Google Analytics 4.
- Ahrefs, Semrush, or equivalent SEO platforms.
- Screaming Frog, Sitebulb, or similar crawling tools.
- Schema.org and JSON-LD structured data.
- Site performance analysis.
- Keyword research and search intent analysis.
- Content briefs and on-page optimization.
- Internal linking and site architecture.
- Competitive SEO analysis.
- Backlink and referring domain analysis.
- GEO, AEO, and AI search visibility analysis.
- LLM citation and brand mention tracking platforms.
- Excel or Google Sheets for SEO analysis and reporting.
- BigQuery, Looker Studio/Data Studio, SQL, and log file analysis preferred.
Physical Requirements:
The physical demands of this position are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
Internal and External Interaction:
This position regularly interacts with the Director of Organic Search, content teams, web and ecommerce teams, analytics, brand marketing, and other cross-functional partners throughout the organization. The role translates SEO and AI search findings into actionable recommendations and partners with stakeholders to improve organic visibility, content performance, technical SEO health, and business outcomes across Career Certified brands.
The incumbent may also work with external SEO platforms, technology providers, partners, and other vendors as needed.
Benefits:
The following benefits are available to eligible full-time employees beginning the first of the month following 30 days of employment:
- Medical Insurance
- Dental Insurance
- Vision Insurance
- Life Insurance
- Short-Term Disability
- Long-Term Disability
- 401(k) Plan Participation
- Additional Ancillary Benefits
Salary Context
This $105K-$110K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Career Certified, 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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($107K) sits 49% below the category median. Disclosed range: $105K to $110K.
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.
Career Certified AI Hiring
Career Certified has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $110K - $110K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 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 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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