# Answer Engine Optimization Lead

**Company:** [MongoDB](https://hotfix.jobs/companies/mongodb)
**Location:** Remote
**Role:** Growth Marketing
**Salary:** $104k – $204k/yr
**Experience:** 8+ years
**Skills:** technical seo, aeo, llm optimization, semantic search, structured data, schema markup, knowledge graphs, RAG, json-ld, semantic html, Python, SQL, conductor, ahrefs, semrush
**Posted:** 2026-07-16

> Lead MongoDB's Answer Engine Optimization (AEO) strategy to maximize visibility and citations in LLMs and AI platforms like ChatGPT, Claude, and Perplexity. Manage a team, optimize technical content architecture for RAG, drive community advocacy, and develop AI-first content frameworks to boost developer mindshare and PLG.

## Job Description

## Key Responsibilities
- Define and execute AEO strategy: Design and own MongoDB’s global LLM visibility growth initiatives. Develop and execute strategies focused on increasing the citation share, sentiment, and accuracy of MongoDB across various technical developer audiences and agentic AI models.
- Team Leadership: Oversee and scale a high-performing team of SEO, AEO, and content optimization professionals, fostering a culture of rapid experimentation in the AI search space.
- Technical Information Architecture: Collaborate with Product, Engineering, and Documentation teams to optimize MongoDB’s digital footprint for LLM crawlers. Impose best practices for structured data, schema markup, knowledge graphs, and API/documentation accessibility to facilitate flawless RAG (Retrieval-Augmented Generation) ingestion.
- UGC Response & Community Advocacy: Coordinate a multi-team framework (including Developer Relations and Support) to ensure timely, high-quality, and LLM-friendly insights are present on critical developer watering holes (e.g., Reddit, Stack Overflow, GitHub, Wikipedia). Leverage "build in public" tactics to organically feed LLM training datasets with high-authority brand mentions.
- Evangelize AI-First Content Frameworks: Create corporate guidelines and train cross-functional SMEs, technical writers, and marketers to write effectively for LLMs (e.g., token-friendly syntax, clear entity relationships, and direct intent fulfillment).
- Advanced Analytics & Attribution: Partner with Data Science and Analytics teams to pioneer new framework metrics for AEO, including Share of Model (SoM), LLM citation impact across third-party sites, and AEO contribution to PLG sign-ups and pipeline.

## Qualifications
- 8+ years of experience in technical SEO, digital strategy, technical content marketing, or growth engineering, with at least 2–3 years specifically focused on emerging AI technologies, LLM optimization, or semantic search. At least 3 years of experience leading an SEO team.
- Proven track record of hiring, leading, and mentoring technical marketing professionals, alongside a demonstrated ability to influence matrixed cross-functional teams (Product, DevRel, Engineering) without direct authority.
- Deep understanding of how LLMs crawl, index, and retrieve information. Familiarity with semantic HTML, JSON-LD, knowledge graphs, and how modern search engines leverage vector spaces and RAG.
- A strong grasp of how software engineers, architects, and technical decision-makers discover products. You understand the cultural nuances required for authentic, high-value engagement in developer communities.
- Exceptional ability to translate complex AI/SEO technicalities into clear, high-level business outcomes and ROI for executive leadership.
- Proficiency with enterprise SEO suites (e.g., Conductor, Ahrefs, SEMrush), modern AEO/LLM monitoring platforms (e.g., Profound), web analytics (Google Analytics/Amplitude), and project management software (Jira).

## Preferred Qualifications
- Experience with developer community and social listening tools (e.g., CommonRoom, Sprinklr, Reddit Mod Tools).
- Background in technical writing or managing technical content pipelines for developer tools, databases, or cloud infrastructure.
- Basic understanding of Python or SQL to assist in scraping, analyzing LLM outputs, or manipulating large datasets.

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