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About This Role
Tombras is seeking an Associate Director, SEO & AI Visibility to help lead the next era of search for our clients.
This is not a traditional SEO role. Search has evolved beyond blue links, rankings, and click-through rate alone. Today, brands are increasingly discovered through AI Overviews, ChatGPT, Gemini, Perplexity, YouTube, Reddit, reviews, maps, and other answer-first environments that shape consumer decisions before a website visit ever happens.
We are looking for a modern search leader who is equally comfortable driving enterprise SEO strategy, building AI-enabled content workflows, guiding GEO/AEO programs, and helping clients measure visibility across both traditional search engines and LLM-driven discovery platforms.
This role sits within Tombras’ Integrated Search team and reports into senior SEO leadership. You will serve as a senior strategic partner to clients across technical SEO, local SEO, content strategy, AI visibility, and organic performance measurement.
Why Join Tombras’ Integrated Search Team?
Tombras is one of the leading independent agencies in North America, built to connect data, creativity, media, and technology in ways that drive real business results.
Our search team has long been recognized for elite performance in traditional SEO and integrated search. Now, we are building what comes next: modern SEO programs designed for a world where consumers increasingly discover brands through answer engines, AI summaries, cited sources, platform-native search, and conversational interfaces.
This is an opportunity to help define that future inside an agency that values deep expertise, innovation, and practical execution.
Where you'll be working: Atlanta, GA, Portland, OR or Knoxville, TN. Relocation Assistance may be provided.
What you'll be doing:
- Lead SEO and AI visibility strategy for national, enterprise, and multi-location brands across technical SEO, content SEO, local SEO, and emerging answer-engine optimization programs.
- Architect modern organic discovery strategies that improve brand visibility across Google Search, AI Overviews, ChatGPT, Gemini, Perplexity, YouTube, Reddit, maps, reviews, and other platforms influencing consumer decision-making.
- Translate shifting search behavior into actionable client strategies, helping brands adapt to zero-click search, cited-answer environments, and AI-mediated discovery.
- Build and refine GEO/AEO frameworks focused on semantic clarity, entity optimization, structured content, source credibility, schema, internal linking, and answer-first content architecture.
- Develop AI-enabled content workflows that improve the speed, consistency, and quality of SEO deliverables, including content briefs, optimization recommendations, page refreshes, FAQ generation, internal linking recommendations, and other scalable workstreams.
- Ensure AI-assisted workflows are grounded in brand truth, first-party inputs, subject matter expertise, and sound editorial judgment rather than generic, low-value content generation.
- Partner with Content, Creative, UX, Development, PR, Social, Analytics, and Paid Search teams to improve the broader signals that influence both search rankings and AI citation visibility.
- Lead technical SEO strategy across crawling, indexation, renderability, schema, canonicals, hreflang, XML sitemaps, redirects, site migrations, internal linking, international architecture, and other core areas of enterprise SEO.
- Oversee local SEO and reputation programs for multi-location brands, including Google Business Profile optimization, listings consistency, duplicate suppression, ratings and reviews, local landing pages, and hyperlocal visibility.
- Monitor and analyze brand presence across traditional search and AI-driven experiences, including citations, mentions, answer inclusion, share of voice, and evolving visibility signals by LLM or platform.
- Help define and socialize new measurement frameworks for organic success, incorporating rankings, traffic, citations, mentions, AI visibility share, branded demand, local actions, and business outcomes.
- Partner with Analytics and Data Science teams to improve reporting tied to AI visibility, answer-engine exposure, and the changing relationship between visibility and clicks.
- Lead client presentations, strategic recommendations, reporting discussions, and testing plans with confidence and clarity.
- Manage workloads and quality across a team of SEO practitioners, providing mentorship, training, feedback, and hands-on support where needed.
- Contribute to department innovation, process improvement, POV development, and new business opportunities related to modern SEO, AI visibility, GEO, and AEO.
What you bring:
- 8+ years of experience in SEO, with meaningful experience leading enterprise SEO initiatives for large or complex brands.
- Strong hands-on expertise in technical SEO, content strategy, local SEO, and organic search performance.
- A clear and practical point of view on how AI Overviews, LLMs, answer engines, and generative discovery are changing search.
- Experience developing or applying GEO/AEO strategies designed to improve visibility in AI-generated results and answer-first environments.
- Experience using AI tools and workflow design to improve SEO productivity, content development, analysis, or execution.
- Strong understanding of structured data, semantic search, entity optimization, content architecture, and the signals that influence both search engines and AI retrieval systems.
- Ability to think beyond traditional SEO KPIs and help clients adopt smarter measurement frameworks tied to citations, mentions, visibility, discoverability, and business outcomes.
- Strong command of enterprise SEO platforms, site crawling tools, analytics tools, and local/listings platforms.
- Excellent client communication skills, including the ability to present complex ideas in a clear, concise, and persuasive way.
- Strong organizational and team leadership skills, with the ability to manage multiple workstreams and mentor junior team members.
- A player-coach mindset with the willingness to stay close to the work, solve hard problems, and push the discipline forward.
Preferred Experience
- Experience with BrightEdge, Conductor, Ahrefs, SEMrush, Screaming Frog, Lumar/Deepcrawl, Google Search Console, GA4, and major local listings/reputation platforms.
- Experience with multi-location brands, enterprise sites, and international or complex website architectures.
- Familiarity with prompt design, retrieval-aware content structuring, entity SEO, schema strategy, and emerging AI visibility tooling.
- Experience building scalable SEO or content workflows that combine human strategy with AI assistance.
- Experience collaborating closely with paid media, analytics, PR, creative, and content teams in an integrated agency environment.
- Familiarity with CMS platforms such as AEM, WordPress, Drupal, Sitecore, or similar enterprise content systems.
- Working knowledge of HTML, CSS, JavaScript, and how modern front-end frameworks affect search accessibility and indexation.
What Success Looks Like:
- Clients see Tombras as a credible strategic leader in both traditional SEO and AI-era visibility.
- SEO programs move beyond rankings alone and evolve toward broader visibility, discoverability, and business-impact measurement.
- AI-enabled workflows make the team faster and smarter without sacrificing quality, accuracy, or strategic thinking.
- Brands improve their visibility not only in search engine results pages, but also in AI-generated answers, cited responses, and platform-native discovery environments.
- Junior team members become stronger strategists and operators under your leadership.
- Tombras continues to strengthen its market position in modern search, local SEO, and AI visibility.
Why you'll want to work at Tombras:
- Family - It comes first, on every list. Tombras has been family-run since day 1, we strive to facilitate a family-oriented environment rooted in supporting one another.
- Dog-friendly offices
- Flexible PTO
- Generous parental leave for primary and non-primary caregivers
- Medical (PPO or High Deductible option) for employee + dependents
- 401(k) Participation
- Employer-paid Dental & Vision
- A company culture of promotions from within and an atmosphere allowing for varied and rapid career development.
Want more reasons to work at Tombras? Check out the latest Tombras News and Our Values.
Tombras is proud to be an equal opportunity employer dedicated to pursuing and hiring a diverse workforce.
Tombras is an E-Verify employer and participates in the E-Verify program.
This job description is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities that are required of the employee. Duties, responsibilities and activities may change or new ones may be assigned at any time with or without notice. Job may require traveling overnight, driving long distances as required and sitting for extended periods of time with occasional walking and standing and occasionally lifting or carrying articles weighing less than 10 pounds.
Remote employees must be based in the United States.
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Tombras, 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 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
Tombras AI Hiring
Tombras has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Atlanta, GA, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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