Director of Search Engine Optimization (SEO) & AI Visibility

$110K - $135K Remote Mid Level AI/ML Engineer

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Skills & Technologies

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About This Role

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About Kind Creative

Kind Creative is a mission-driven marketing agency dedicated to elevating healthcare companies through data-driven, transparent, and altruistic digital marketing strategies. Our work helps healthcare providers connect with the people who need them most — often during critical, life-changing moments. We specialize in behavioral health, addiction treatment, and purpose-driven healthcare spaces where empathy, trust, and clarity are non-negotiable.

At Kind, we’re not just marketers — we’re collaborators, builders, and advocates for better outcomes. We believe great marketing starts with understanding people. That’s why we lead with empathy, strategy, and creative courage — crafting campaigns that not only perform, but resonate.

Our culture is deeply people-first, both in the way we serve our clients and in how we support our team. We value curiosity, integrity, and kindness as much as we value performance. We’re a remote-friendly, growth-focused team that believes in healthy boundaries, open communication, and doing work that genuinely matters.

If you’re looking for a place where impact meets intention, where your ideas will be heard, and where you’ll help reshape how healthcare brands connect with their communities — we’d love to meet you.

Director of SEO Position Overview

Kind Creative is seeking a strategic, hands-on Director of SEO to lead our organic search department and drive measurable growth for our clients. This is a player-coach role: the Director will set the department’s vision, manage and develop the SEO team, own client relationships, and remain directly involved in high-level strategy, analysis, and execution.

The Director will oversee both traditional search engine optimization and the rapidly evolving field of AI-driven organic discovery, including AI Overviews, conversational search, answer engines, large language models, and other emerging platforms that influence how people find, evaluate, and select brands.

This person will help evolve Kind Creative into a technology-first SEO agency by creating scalable services, standardized workflows, intelligent automation, and repeatable strategies that produce strong client outcomes. The responsibilities of this role will continue to evolve as search technology, client needs, and the agency grow.

Who Will Thrive in This Role

This role is likely to be a strong fit for someone who:

  • Enjoys being both a department leader and a hands-on SEO strategist.
  • Thinks in terms of business outcomes rather than rankings alone.
  • Can move comfortably between client strategy, technical analysis, team coaching, and operational planning.
  • Is curious about AI-driven search while remaining appropriately skeptical of hype and unsupported claims.
  • Creates structure in a growing environment and follows through on the systems they introduce.
  • Communicates proactively, especially when performance is changing, expectations are unclear, or an account may be at risk.
  • Takes ownership of results and raises risks early.
  • Can prioritize high-impact work instead of treating every request as equally urgent.
  • Values documentation, process consistency, accurate task management, and quality assurance.
  • Gives clear feedback, develops others, and welcomes informed disagreement.
  • Collaborates well across disciplines and can translate between technical, creative, and business teams.
  • Is energized by building a department, not merely maintaining one.

Who May Not Do Well in This Role

This role is unlikely to be a good fit for someone who:

  • Prefers individual execution and does not enjoy coaching, delegating, reviewing work, or holding a team accountable.
  • Focuses primarily on rankings or traffic without connecting performance to leads, conversions, revenue, and client objectives.
  • Avoids difficult client conversations or waits for problems to escalate before communicating them.
  • Chases every new AI or SEO trend without evaluating evidence, risk, scalability, or business value.
  • Is resistant to AI, automation, or changes in how people discover information online.
  • Relies on AI-generated work without careful review, fact-checking, and professional judgment.
  • Struggles with documentation, task ownership, process adherence, or maintaining accurate project-management systems.
  • Prefers a highly established environment in which responsibilities, processes, and service offerings rarely change.
  • Has difficulty balancing strategic priorities with deadlines, capacity constraints, and operational details.
  • Works primarily within departmental silos or struggles to collaborate with content, development, paid media, sales, and analytics teams.
  • Is uncomfortable being accountable for client satisfaction, team performance, delivery quality, and department growth.

Key Responsibilities SEO and Organic Search Leadership

  • Own and develop comprehensive organic search strategies across client accounts, aligning recommendations with each client’s business goals, growth priorities, audience, and success metrics.
  • Establish the strategic direction of the SEO department and ensure that client work reflects current best practices across technical SEO, content, authority building, local search, user experience, and conversion.
  • Conduct and oversee SEO audits, competitive analyses, keyword and topic research, opportunity assessments, and performance reviews.
  • Translate complex search data into clear priorities, practical recommendations, and compelling client narratives.
  • Monitor search engine updates, emerging technologies, competitive developments, and changes in user search behavior.
  • Partner with company leadership to position SEO and organic discovery as central components of Kind Creative’s service offering and long-term growth.

AI and Emerging Organic Discovery

  • Develop and continually refine strategies for visibility in AI-mediated search experiences, including AI Overviews, answer engines, conversational search tools, and large language model responses.
  • Help clients build the technical clarity, topical authority, entity recognition, structured information, trustworthy content, and third-party validation needed to compete across both traditional and AI-driven discovery.
  • Evaluate how clients and competitors are represented, cited, summarized, and recommended in emerging AI search environments.
  • Develop practical methods for measuring AI search visibility, referral activity, brand mentions, citations, assisted conversions, and other meaningful indicators as the available measurement tools evolve.
  • Ensure that AI search initiatives remain grounded in strong SEO fundamentals, reliable information, audience needs, and measurable business outcomes.
  • Identify responsible uses of generative AI, agents, and automation throughout the department while maintaining human review, factual accuracy, brand integrity, data security, and HIPAA-aware processes where applicable.
  • Educate clients, team members, and leadership on changes in AI-driven search without relying on hype or presenting unproven tactics as established best practices.

Client Strategy, Service, and Retention

  • Serve as the strategic lead for assigned SEO clients. Long term goal is for you to build the team and sit in a Director revolve vs staying in client delivery.
  • Lead client calls and ensure clients understand their strategy, progress, risks, and next steps.
  • Maintain a proactive communication rhythm that strengthens client confidence, satisfaction, and retention.
  • Identify performance issues, communication gaps, and other signs of account risk early, then develop and implement corrective action plans.
  • Identify opportunities to expand client engagements when additional SEO or cross-department services would meaningfully support the client’s goals.
  • Balance client expectations with available resources, technical realities, and responsible timelines.
  • Maintain a hands-on understanding of each client’s work rather than operating solely as a high-level relationship manager.

Technical, On-Page, and Off-Page SEO

  • Oversee keyword research, search-intent analysis, content optimization, internal linking, digital authority building, local SEO, and user-experience recommendations.
  • Ensure client websites follow best practices for architecture, crawling, rendering, indexing, canonicalization, redirects, structured data, Core Web Vitals, page speed, and mobile usability.
  • Work closely with developers to scope, prioritize, implement, and verify technical recommendations.
  • Diagnose indexing, visibility, traffic, engagement, and conversion issues using reliable data from platforms such as Google Search Console, Google Analytics, Ahrefs, Semrush, and relevant crawling and content tools.
  • Conduct regular quality-control reviews and ensure recommendations comply with search engine guidelines.
  • Maintain awareness of privacy, security, accessibility, and HIPAA considerations that may affect SEO strategy, analytics, content, tracking, and client engagements.

Content Strategy

  • Partner with the content team to build strategies informed by search demand, audience needs, business objectives, competitive gaps, and the client’s subject-matter expertise.
  • Ensure content briefs and recommendations clearly address search intent, topical coverage, brand differentiation, conversion goals, and opportunities for both traditional and AI-driven discovery.
  • Help clients create useful, accurate, original content that demonstrates genuine experience and expertise.
  • Coordinate content refreshes, consolidation, pruning, and expansion based on performance and strategic value.

Analytics, Reporting, and Accountability

  • Define the KPIs that matter for each client and connect SEO activity to qualified traffic, leads, conversions, revenue, visibility, and other business outcomes.
  • Develop and maintain scalable reporting systems, automated alerts, dashboards, and regular performance reviews.
  • Monitor organic visibility, rankings, click-through rates, engagement, conversions, technical health, AI visibility, and other relevant leading and lagging indicators.
  • Partner with paid media and attribution teams to ensure organic search data is captured, integrated, and interpreted accurately.
  • Distinguish meaningful performance changes from normal volatility and communicate the difference clearly.
  • Maintain accountability for results while recognizing when outcomes depend on client approvals, development resources, market conditions, or other external factors.

Team Leadership and Development

  • Hire, onboard, train, coach, and retain a high-performing SEO team.
  • Build a culture of strategic thinking, curiosity, accuracy, collaboration, accountability, and psychological safety.
  • Lead recurring SEO team meetings and cross-department planning sessions.
  • Set clear expectations, review work, provide actionable feedback, and create advancement opportunities for high-potential team members.
  • Ensure the team stays current on SEO, AI systems, analytics, industry developments, client-specific requirements, and HIPAA considerations.
  • Balance team capacity and utilization to prevent bottlenecks, missed deadlines, and unsustainable workloads.
  • Ensure deliverables are accurate, actionable, and client-ready before they are presented or implemented.

Operations and Process Management

  • Standardize repeatable workflows for audits, keyword research, content briefs, technical recommendations, 90-day plans, monthly reporting, implementation reviews, and client communication.
  • Ensure SEO work is properly scoped, prioritized, assigned, tracked, and maintained in ClickUp.
  • Maintain a healthy backlog in which work reflects the approved strategy, dependencies are visible, priorities remain current, and outdated or unowned tasks do not accumulate.
  • Build and maintain an internal knowledge base containing SOPs, playbooks, industry updates, client context, quality standards, and lessons learned.
  • Improve team throughput and operational efficiency without compromising accuracy, strategy, client care, or work quality.
  • Resolve cross-functional dependencies that could delay deliverables or affect client outcomes.

Service Development and Agency Growth

  • Define and document Kind Creative’s SEO-related service offerings and SKUs.
  • Establish initial time estimates, deliverables, ownership, and success criteria for each offering.
  • Use accurate time-tracking and delivery data to improve forecasting, pricing, staffing, and profitability.
  • Support the sales team with discovery, opportunity evaluation, scoping, audits, attribution planning, and initial and ongoing 90-day plans.
  • Forecast staffing and resource needs based on client commitments, department capacity, and planned growth.
  • Oversee SEO budgets for client and internal initiatives, allocating resources according to expected impact.
  • Collaborate with sales, development, content, paid media, attribution, and internal marketing teams.
  • Support SEO for Kind Creative’s own website and marketing initiatives so the agency’s presence reflects the quality of its client work.
  • Identify opportunities to build or adopt software, integrations, agents, and automation that make SEO delivery more effective and scalable.
  • Lead the testing, rollout, documentation, and adoption of approved technologies.

Measures of Success

Success in this role will be evaluated through a balanced set of client, team, operational, and business outcomes, including:

  • Organic visibility, qualified traffic, leads, conversions, and client-specific business results.
  • Client satisfaction, retention, communication quality, and early resolution of account risks.
  • Progress in AI-driven organic visibility and the development of credible measurement practices.
  • On-time delivery and predictable completion of SEO commitments.
  • Quality-control performance, accuracy, and reduction of avoidable rework.
  • Sustainable team utilization, capacity planning, engagement, and retention.
  • Adoption of SOPs, ClickUp workflows, reporting standards, and automation.
  • Backlog quality, prioritization, and delivery readiness.
  • Effective resolution of cross-department dependencies.
  • Completion of quarterly and annual strategic initiatives.
  • Development of scalable, clearly defined, and financially sound SEO offerings.
  • Responsible adoption of tools and automation that improve efficiency or client impact.

Qualifications and Capabilities

The strongest candidates will bring:

  • Significant hands-on experience developing and executing SEO strategies for multiple clients, brands, or business units.
  • Strong knowledge of technical SEO, content strategy, analytics, local search, authority building, and conversion-focused optimization.
  • An informed understanding of AI-driven search and the ways organic discovery is expanding beyond traditional search results.
  • Experience leading people, reviewing work, developing processes, and improving team performance.
  • Confidence communicating with clients, executives, developers, content specialists, sales teams, and other stakeholders.
  • The ability to connect technical and marketing activity to meaningful business outcomes.
  • Experience with Google Search Console, Google Analytics, crawling platforms, keyword and competitive research tools, reporting systems, and project-management software.
  • Sound judgment about when to automate, when human expertise is essential, and how to evaluate AI-generated outputs.
  • Strong prioritization, documentation, quality-control, and operational-management skills.
  • The ability to manage competing demands without allowing communication, accuracy, or follow-through to deteriorate.
  • Comfort working in a growing agency where responsibilities, systems, and service offerings will continue to evolve.

Job Type: Full-time

Pay: $110,000.00 - $135,000.00 per year

Application Question(s):

  • What is your most recent random act of kindness?
  • What’s a recent decision you made that you’re proud of, and what did you learn from it?
  • How would you align the SEO team with Paid Media, CRO, and Content teams to hit business-level goals?
  • How quickly would you be able to start at Kind Creative?
  • What’s your take on the current role of AI in SEO and what will separate great SEOs from average ones over the next 2 years?

Work Location: Remote

Salary Context

This $110K-$135K 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

Company Kind Creative
Title Director of Search Engine Optimization (SEO) & AI Visibility
Location Remote, US
Category AI/ML Engineer
Experience Mid Level
Salary $110K - $135K
Remote Yes

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 Kind 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

Semrush

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. Director-level AI roles across all categories have a median of $272,250. This role's midpoint ($122K) sits 42% below the category median. Disclosed range: $110K to $135K.

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.

Kind Creative AI Hiring

Kind Creative has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $135K - $135K.

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

Based on 11,866 roles with disclosed compensation, the median salary for AI/ML Engineer positions is $212,000. Actual compensation varies by seniority, location, and company stage.
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.
About 6% of the 8,943 AI roles we track offer remote work. Remote availability varies by company and seniority level, with senior and leadership roles more likely to offer location flexibility.
Kind Creative is among the companies actively hiring for AI and ML talent. Check our company profiles for detailed breakdowns of open roles, salary ranges, and hiring trends.
Common next steps from AI/ML Engineer positions include ML Architect, AI Engineering Manager, Principal ML Engineer. Progression depends on whether you lean toward technical depth, people management, or product strategy.

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