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About This Role
*Lead SEO, CRO, and emerging AI/Generative search (GEO) to drive organic performance.*
The Role: Head of Organic Growth (SEO / CRO / AI Search)
Employment: Full\-Time
Must be eligible to work in the USA and located in one of the following states: TN, GA, AL, FL, NC, SC, NV, NH, TX, MN, VA, IL
The Facts: Johnson Group helps brands slay the giants that stand in the way of their success by blending fresh ideas with proprietary direct response tools to forge deeper brand connections and more profitable conversions. We provide full\-funnel marketing and advertising from brand positioning, research, data science and full\-stack development to video/photo content, and strategy and creative.
The Gist: If you are looking for a team instead of a holding company and you dream about the perfect blend of collaboration and autonomy, keep reading. Johnson Group is a place where every person is truly a part of building what the future of the agency looks like. Everyone is empowered to Dig for Big. This means we are looking for big skills, big outside\-the\-box thinking, and big passion for mastering the details that drive client success.
The Summary: We are looking for a strategic, player\-coach leader to join our team as Head of Organic Growth (SEO / CRO / AI Search). This is a director\-level role that owns the agency’s organic performance discipline end\-to\-end across traditional SEO, Conversion Rate Optimization (CRO), and the rapidly emerging field of AI / Generative search (also called GEO or AEO). The ideal candidate has 7\+ years of progressive experience across SEO and CRO, with mandatory hands\-on expertise in Google Analytics 4, Google Search Console, Google Tag Manager, an enterprise A/B testing platform (VWO, Optimizely, or Convert), and a working command of how AI\-driven surfaces (Google AI Overviews, ChatGPT, Perplexity, Claude) source and cite content. This Director will set the organic strategy across the client portfolio, lead and mentor SEO/CRO/GBP specialists, partner with Account Directors and the Development Team on execution, and personally own the agency’s POV on where organic growth is going next. A track record of measurable organic lift across rankings, qualified traffic, AI citations, conversion rate, and revenue is essential.
The Responsibilities:
Organic Growth Strategy \& Leadership: Set and own the agency’s organic growth strategy across SEO, CRO, and AI/Generative search. Translate client business goals into integrated organic roadmaps that ladder up to measurable revenue impact. Lead, mentor, and grow a team of SEO, CRO, and GBP specialists by setting standards, reviewing work, and developing career paths. Serve as the agency’s senior point of contact for organic strategy in new business pitches, QBRs, and executive\-level client conversations.
SEO Strategy \& Execution: Direct technical, on\-page, content, and off\-page SEO programs across a multi\-client portfolio. Oversee site audits, keyword and topic strategy, internal linking, schema/structured data, site architecture, and authority\-building. Partner with the Development Team on technical SEO requirements (Core Web Vitals, indexability, rendering, hreflang, structured data) and with content teams on topical authority and editorial planning.
CRO \& Experimentation Leadership: Own the agency’s experimentation discipline. Set the testing roadmap, prioritization framework, and statistical rigor across client A/B and multivariate testing programs. Personally design and review high\-stakes experiments. Partner with the CRO Specialist on platform execution (VWO, Optimizely, Convert), ensure clean tracking via GA4 and GTM, and translate test outcomes into permanent site changes and case studies.
AI / Generative Search (GEO / AEO): Build and own JG’s methodology for getting clients cited in AI\-driven search surfaces such as Google AI Overviews, ChatGPT, Perplexity, Claude, Gemini, and what comes next. Develop the agency’s POV on content structure, entity optimization, citation\-worthiness, and tracking AI visibility. Lead client education on the GEO/AEO shift and what it means for their organic strategy.
Platform \& Tool Expertise:
Google Analytics 4, Search Console \& Google Tag Manager (Required): Deep proficiency in GA4 for funnel and acquisition analysis, Search Console for query and indexing analysis, and GTM for event tracking and dataLayer architecture is required.
A/B Testing Platforms (Required): Hands\-on, leader\-level experience with at least one enterprise\-grade testing platform such as VWO, Optimizely, or Convert. Must be able to architect a testing program and personally QA variations, not just direct one.
Enterprise SEO Tooling (Required): Working command of an enterprise SEO toolkit such as Ahrefs, Semrush, Screaming Frog, Sitebulb, or equivalent. Must be comfortable running a technical crawl, log\-file review, and building a topic/cluster strategy from scratch.
AI Search Visibility Tooling (Required / Differentiator): Working knowledge of tools and methods for monitoring AI Overview presence, LLM citations, and brand mentions inside generative answer engines (e.g., Profound, Peec, Otterly, Athena, or equivalent). Must have a clear point of view on what works.
JavaScript, HTML \& CSS (Required): Working proficiency in front\-end web technologies to scope test variations, troubleshoot tracking, and partner credibly with the Development Team. Need not be a production engineer, but must be comfortable reading code and writing/debugging scripts inside testing and tag platforms.
Figma (Required): Ability to collaborate within Figma to review, annotate, and contribute to wireframes and UX/UI explorations that inform test variations and landing page optimizations.
Heatmap \& Session Recording Tools (Preferred): Experience with Microsoft Clarity, Hotjar, or FullStory to drive qualitative insight and test hypotheses.
CMS Platforms (Preferred): Familiarity with WordPress and/or Shopify for shipping optimized pages and CRO changes inside common CMS environments.
Local SEO Tooling (Preferred): Familiarity with Soci, Yext, or equivalent for multi\-location clients, sufficient to direct a GBP Specialist credibly.
UX Research \& CRO Best Practices: Direct heuristic evaluations, usability audits, and journey mapping to identify conversion barriers. Apply persuasion principles, cognitive bias awareness, form optimization, page speed impact, and mobile\-first design. Partner with UX/UI designers and developers to ensure test variations and permanent changes meet both conversion goals and brand standards.
Performance Reporting \& Client Communication: Own the organic performance narrative across the portfolio. Build and present quarterly business reviews that connect SEO/CRO/GEO activity to revenue. Proactively flag pacing or performance concerns to Account Directors and the broader strategy team. Develop case studies that demonstrate the ROI of organic work and translate statistical outcomes into business impact.
Strategy \& Innovation: Stay at the front of search, experimentation, and behavioral analytics. Build JG’s POV on how AI\-powered personalization, predictive analytics, and generative answer engines change organic strategy. Contribute to the agency’s external thought leadership and internal best practices.
Qualifications:
- 7\+ years of progressive experience across SEO and CRO, with at least 2 years in a lead, manager, or director\-level role is a necessary requirement for this role.
- Verifiable, hands\-on experience with Google Analytics 4 (GA4\), Google Search Console, and Google Tag Manager (GTM) is mandatory; candidates without this experience will not be considered.
- Demonstrated proficiency in at least one enterprise A/B testing platform (VWO, Optimizely, or Convert) is required.
- Demonstrated proficiency in an enterprise SEO toolkit (Ahrefs, Semrush, Screaming Frog, Sitebulb, or equivalent) is required.
- A clear, evidence\-based point of view on AI / Generative search (GEO / AEO) is required, including how content gets cited by Google AI Overviews, ChatGPT, Perplexity, Claude, and Gemini.
- Working knowledge of JavaScript, HTML, and CSS sufficient to scope test variations, troubleshoot tracking, and partner credibly with developers is required.
- Proficiency in Figma for design collaboration and wireframe review is required.
- Strong understanding of CRO best practices, including statistical significance, sample size calculation, and experiment design methodology is essential.
- Experience with heatmap and session recording tools (Microsoft Clarity, Hotjar, FullStory) is strongly preferred.
- Agency\-side experience managing multi\-client portfolios is strongly preferred.
- Experience leading or mentoring SEO, CRO, or local search specialists is strongly preferred.
- A college degree in Marketing, Analytics, Computer Science, UX Design, Business, or a related field is preferred.
- Exceptional organizational and time\-management skills, with a proven ability to lead multiple concurrent client engagements simultaneously.
- Strong analytical skills and the ability to translate complex data into actionable, evidence\-based recommendations.
- A curious and proactive, team\-oriented attitude and a genuine passion for understanding user behavior and driving measurable business outcomes.
The Benefits:
- Flexibility to work from home with optional hybrid work environment for locals in our beautifully renovated downtown office (with paid parking)
- 100% employer\-paid health, dental, and vision insurance for employee AND two family members. (Oh yes, you read that right. 100%. Employer. Paid.)
- 401k with company match
- PTO, sick days, and paid holidays
- Wellness stipend
- Professional development opportunities
- Company\-wide social events like an annual lake day, team\-specific retreats, and more
- Summer Fridays
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 Johnson Group, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
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.
Johnson Group AI Hiring
Johnson Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% above the national 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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