GEO Expert – AI Search Optimization for Crypto

About the role

Gate is looking for a GEO Expert to shape the company’s presence in AI‑generated search results such as ChatGPT, Perplexity, Gemini and emerging AI platforms. This is a greenfield opportunity to build the strategy, content system and measurement framework from scratch and own the end‑to‑end results.

Key responsibilities

• Drive Gate’s recommendation frequency inside AI‑generated responses across major AI search products.
• Build a content system for AI ingestion, including structured data schemas, authoritative reference pages, FAQ layers and citation‑ready assets.
• Design and maintain a dashboard that tracks Gate’s AI share‑of‑voice on high‑value crypto and Web3 queries and benchmark against competitors.
• Partner with SEO, Content and PR teams to extend GEO coverage across Gate’s core verticals (futures, Launchpad, staking, Web3 wallet, etc.).
• Stay abreast of AI search product evolution—RAG architectures, model updates, prompt‑based discovery—and continuously adapt strategy.

Required profile

• Proven hands‑on experience in GEO, AI search optimization or a closely related field.
• Strong SEO foundation covering technical SEO, structured data, content strategy and keyword research.
• Deep understanding of LLM retrieval, ranking and citation mechanisms.
• Ability to create measurement frameworks from scratch and translate ambiguous signals into clear priorities.
• Fluent knowledge of the crypto and Web3 ecosystem (exchanges, futures, DeFi, wallets, Launchpad).
• Experience collaborating cross‑functionally with content, PR and SEO teams without direct authority.
• Self‑directed, comfortable in 0→1 environments and able to set your own roadmap.
• Fluent in English and Mandarin.
• Bachelor’s degree or higher in Marketing, Computer Science, Communications or related field.
• Background from a top‑tier crypto exchange or crypto‑native media is a plus.

Required skills

• Technical SEO
• Structured data implementation
• AI search optimization
• LLM retrieval and ranking knowledge
• RAG (Retrieval‑Augmented Generation) architectures