Generative Engine Optimization Analyst

About Metagenics

Metagenics believes in helping people live happier, healthier lives by realizing their genetic potential. It’s why, when we defined Metagenics’ Mission, Values, and Vision, we started with our company’s own DNA. United by purpose and core values (Integrity, Authenticity, Respect, Diversity & Inclusion and Healthy & Happiness) the Metagenics’ team is dedicated to providing effective nutritional products and solutions, for healthcare professionals.

People Culture First We believe the way we do business is as important as the business we do; that a company in the nourishment business should nourish its people, too. So, we incorporated healthy, sustainable care into every dimension of our culture. Our diverse and expansive team are a prime example of the power of a people-first approach. We know first‑hand, when an organization prioritizes internal growth and fosters empathy, its people come together to set an example of what the world can become.

The Generative Engine Optimization Analyst will lead Metagenics’ strategy to optimize brand visibility within AI answer surfaces and generative systems. This role blends content strategy, prompt engineering, data & analytics, and experimentation to help ensure that our content is surfaced, cited, and trusted in AI-generated responses. The ideal candidate is deeply curious about how LLMs source information, how citations are chosen, and how content can be structured to be preferentially picked up by generative systems.

• Define and own the roadmap for Metagenics’ visibility in AI answer systems (ChatGPT, Perplexity, Gemini, Claude, etc.).
• Identify thematic and content opportunity areas (topics, health conditions, product categories) that are likely to be queried by users via generative systems.
• Align generative answer optimization (GEO) strategy with broader SEO, content, brand, and regulatory goals.

• Design content frameworks, templates, and prompt formats optimized for AI systems (e.g. question-answer formatting, chunking, context cues).
• Embed AI-relevant metadata, “citation cues,” and structured signals that increase the probability of being referenced by models (e.g. semantic anchors, topical hierarchies).
• Apply structured data (e.g. JSON-LD, schema.org, “llms.txt” or equivalent) or AI metadata where applicable to guide model indexing or citation.
• Work with content authors to craft or adapt content that is “AI-friendly” (clear explanatory style, sourceable claims, modular and atomic building blocks).

• Develop and execute experiments to test prompt formats, content chunking strategies, metadata cues, context windows, and citation likelihood.
• Monitor how various generative systems are citing, summarizing, and referencing content across topics, and reverse engineer “why this content was selected.”
• Use internal analytics, logs, and third‑party tools to monitor when and how Metagenics content is surfaced in AI answer surfaces, and to detect gaps or biases.

• Define key success metrics (e.g. mention/citation frequency, share of AI answers, click through from AI responses, downstream site engagement).
• Track shifts over time in model behavior, prompt trends, and system updates (e.g. new LLMs, citation strategies).
• Produce regular dashboards and executive reports showing performance, insights, and recommendations.

• Partner with SEO, content, UX, engineering, legal/regulatory, and medical teams to ensure content is both optimized for AI systems and compliant with health / scientific standards.
• Educate internal stakeholders (writers, content leads, product teams) on best practices for generative answer optimization.
• Maintain guidelines, playbooks, and version control of prompt templates, metadata frameworks, and experiment protocols.

Required:

• 3–5+ years in SEO, content strategy, digital marketing, or AI/ML specialization with direct experience or curiosity in content optimization for generative systems.
• Familiarity with how large language models (LLMs)…