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About This Role
Job Description: SEO \& AI Search Strategist (On\-Keyboard)
Location: Remote with two office workdays per month
Department: Digital Marketing
Experience: 3\-6 years
Reports To: Director of Marketing and Head of Growth and Performance
Salary Range: $70k\-$90k
Benefits: Health insurance, Vision \& Dental, PTO
About Us
At Junction Creative, we help businesses grow through strategic marketing that delivers measurable results. We combine data\-driven SEO, paid media, web development, AI\-powered marketing, and creative strategy to help clients dominate their markets.
As search evolves from traditional Google rankings to AI\-powered answer engines, we're looking for an SEO professional who is passionate about staying ahead of the industry and helping clients succeed in the era of AI search.
Position Overview
We are seeking a highly motivated, hands\-on SEO Specialist who will execute day\-to\-day SEO initiatives while helping clients gain visibility across both traditional search engines and emerging AI platforms.
This is an "on\-keyboard" production role which will heavily utilize SEMRush and similar platforms. You will be responsible for implementing SEO strategies, conducting technical audits, creating optimization plans, executing content improvements, tracking performance, and helping clients become visible within AI\-generated search experiences.
The ideal candidate understands that SEO is rapidly evolving beyond Google rankings into AI\-powered search platforms such as ChatGPT, Google AI Overviews, Perplexity, Claude, Gemini, and other large language model (LLM) search experiences.
Very few people check every single box, and we don't expect you to. What matters more is curiosity and a willingness to build the skills you don't have yet. We're committed to our employees' growth, so if this role excites you, we'd like to hear from you.
Primary Responsibilities
Traditional SEO
- Perform comprehensive SEO audits
- Conduct keyword research and search intent analysis
- Build content strategies
- Optimize website architecture
- Improve on\-page SEO
- Optimize metadata
- Internal linking strategy
- Schema markup implementation
- Technical SEO recommendations
- XML sitemap optimization
- Robots.txt management
- Core Web Vitals optimization
- Crawl error analysis
- Backlink analysis
- Competitor SEO analysis
- Local SEO optimization
- Google Business Profile optimization
- Monitor rankings and organic traffic
- Prepare monthly reporting
- Implement SEO recommendations in WordPress and other CMS platforms like Wix.
AI Search Optimization (AIO)
Help clients increase visibility within AI\-powered search experiences by:
- Optimizing websites for Google AI Overviews
- Improving visibility in ChatGPT responses
- Optimizing for Perplexity AI
- Optimizing for Claude
- Optimizing for Gemini
- Structuring content for LLM consumption
- Implementing semantic SEO best practices
- Creating entity\-based content strategies
- Developing topical authority
- Optimizing structured data
- Improving E\-E\-A\-T signals
- Building knowledge graph relevance
- Identifying opportunities for AI citation and attribution
- Monitoring AI search visibility and brand mentions
- Staying current on rapidly changing AI search algorithms and best practices
Content Optimization
- Optimize existing website content
- Create SEO briefs for content writers
- Perform content gap analysis
- Refresh aging content
- Optimize landing pages
- Improve conversion\-focused copy
- Recommend new content opportunities
- Collaborate with copywriters and designers
Analytics \& Reporting
- GA4
- Google Search Console
- Bing Webmaster Tools
- Ahrefs
- SEMrush
- Screaming Frog
- Looker Studio
- BrightLocal (preferred)
- AI visibility tracking tools (Profound, Goodie, Peec AI, or similar platforms)
- Monitor KPIs and provide actionable insights
Client Communication
- Participate in client strategy meetings
- Explain SEO recommendations in non\-technical language
- Present monthly reporting
- Answer client questions
- Collaborate with account managers
- Coordinate with PPC and web development teams
Required Qualifications
- 3\+ years of hands\-on SEO experience
- Proven success growing organic traffic
- Strong understanding of technical SEO
- Experience with WordPress
- Strong analytical skills
- Excellent written communication
- Experience with GA4
- Experience with Google Search Console
- Familiarity with schema markup
- Understanding of Core Web Vitals
- Strong attention to detail
- Ability to manage multiple client accounts
Preferred Qualifications
- Experience optimizing websites for AI search visibility
- Experience with AI Overviews optimization
- Understanding of LLM optimization (LLMO)
- Familiarity with entity SEO
- Experience using AI tools for SEO workflows
- Agency experience preferred
- Experience working with local and national SEO campaigns
- Experience with enterprise SEO is a plus
AI Technology Experience
The ideal candidate should have practical experience using tools such as:
- ChatGPT
- Claude
- Gemini
- Perplexity
- Google Search Console
Success Metrics
Success in this role will be measured by:
- Growth in organic traffic
- Increase in qualified leads
- Improved keyword rankings
- Increased AI search visibility
- Growth in Google AI Overview appearances
- Technical SEO improvements completed
- Organic conversion improvements
- Client retention
- Client satisfaction scores
- On\-time completion of deliverables
What We're Looking For
You are someone who:
- Loves solving SEO challenges
- Enjoys diving into analytics
- Is curious about emerging AI technologies
- Continuously learns and tests new strategies
- Thrives in a fast\-paced agency environment
- Can balance technical work with client communication
- Takes ownership of client success
- Wants to be at the forefront of the future of search
Why Join Junction Creative?
- Work on diverse client accounts across multiple industries
- Be part of an agency embracing the future of AI\-driven marketing
- Opportunity to help shape our AI search optimization practice
- Collaborative, supportive team environment
- Competitive salary and benefits
- Professional development and continuing education
- Opportunity for career growth
Bonus Skills (Highly Desired)
Candidates with experience in the following areas will stand out:
- Programmatic SEO
- Prompt engineering for SEO workflows
- Digital PR and authority building
- JavaScript SEO
- SEO automation using AI
- CRO (Conversion Rate Optimization)
- Basic HTML/CSS
- Experience with enterprise AI search monitoring platforms
Pay: $70,000\.00 \- $90,000\.00 per year
Benefits:
- Dental insurance
- Flexible schedule
- Health insurance
- Paid time off
- Professional development assistance
- Referral program
- Vision insurance
Experience:
- SEO: 3 years (Required)
Work Location: Hybrid remote in Atlanta, GA 30328
Salary Context
This $70K-$90K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Junction Creative, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($80K) sits 63% below the category median. Disclosed range: $70K to $90K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Junction Creative AI Hiring
Junction Creative has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Atlanta, GA, US. Compensation range: $90K - $90K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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