Right Side Up
Generative Engine Optimization (GEO) Consultant
Austin, TX · Contract
Sponsorship not specified$10k-$40kDetected 265 days ago
LLMsSEOContent MarketingBrand StrategyAffiliate Marketing
About the role
- We're seeking freelance Generative Engine Optimization (GEO) consultants to help our clients navigate the evolving landscape of AI-powered search and discovery.
- This role requires a unique blend of brand strategy, content expertise, and technical optimization skills to ensure our clients' content is visible and impactful across Large Language Models (LLMs) like ChatGPT, Claude, Perplexity, and emerging AI platforms.
Responsibilities
- Develop overarching GEO strategies that combine content optimization, syndication, and analytics.
- Create evaluation frameworks for measuring LLM visibility and impact.
- Build content strategies based on brand segmentation and audience expertise.
- Develop and produce optimized content specifically designed for AI discovery.
- Create content that answers the questions your target audience is asking AI assistants.
- Collaborate with development teams to implement technical changes.
- Develop syndication strategies across Reddit, YouTube, Quora, Discord, and other platforms.
- Leverage affiliate marketing and organic community building.
Requirements
- Strong understanding of content strategy and brand positioning.
- Experience with content syndication across multiple platforms (Reddit, YouTube, Quora, etc.).
- Familiarity with technical SEO principles and their application to GEO.
- Analytics mindset with ability to measure and report on visibility metrics.
- Familiarity with LLM tracking tools (Profound, Scrunch, AirOps, etc.).
- Why Right Side Up Join a team that values expertise, flexibility, and meaningful work with some of the most innovative brands in the world.
Skills
- Contract projects typically range from 10-40 hours per week, depending on client needs and project scope.
Compensation
- $10k-$40k
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This listing is sourced directly from Right Side Up's careers page and normalized into a canonical job model.