Jobs and Careers
OR
Technical Product Marketing AI Engine Optimization Specialist—Oracle AI Database
OracleUnited States, United Statesfull_timeVerifiedPosted 13 Mar 2026
💰 $199,500/yr($82,500/yr – $199,500/yr)
About the role
Role Overview As a Technical Product Marketing AI Engine Optimization (AEO) Specialist, you will be responsible for the strategy that makes Oracle AI Database technologies discoverable, accurately represented, and consistently cited by large language models (LLMs) in the public domain. You’ll optimize the full AI retrieval pipeline, from how product knowledge is structured and published, to how it’s embedded, retrieved, and surfaced in AI-generated answers—so that capabilities such as AI Vector Search and Globally Distributed Database appear with high relevance, strong factuality, and low latency across AI-powered conversational search experiences. This role blends information retrieval, semantic search, content engineering, and LLM evaluation. You’ll partner across Oracle AI Database Product Management, Engineering, and Product Marketing to build a measurable AEO program that drives organic adoption by ensuring Oracle AI Database technical product content is the easiest for AI systems to retrieve and trust.
Key Responsibilities AI Discoverability & Retrieval Optimization
- Audit and optimize how Oracle AI Database documentation, reference content, and knowledge stores are structured for indexing, embedding, and retrieval by LLM-driven systems.
- Define and implement best practices for metadata, content taxonomies, canonical sources, schema/structured data, and machine-readable formatting to improve extraction and grounding.
- Develop strategies for improving semantic retrieval performance.
- Design prompt patterns and evaluation prompts to increase retrieval accuracy, coherence, and factuality, with a focus on reducing hallucinations and improving citation quality.
- Identify high-value prompts, topics, and query clusters where AI-generated answers underperform, and translate gaps into content + technical plans.
- Build AEO reporting and measurement frameworks using KPIs, including retrieval relevance, answer accuracy, citation quality, coverage, and latency.
- Run structured experiments (including A/B tests) on content formats and retrieval strategies; analyze outcomes and operationalize learnings into repeatable playbooks.
- Benchmark Oracle visibility and accuracy versus competitive narratives in AI-generated responses; track drift as AI models and ranking behaviors evolve.
- Partner with Oracle AI Database Product, Engineering, and Product Marketing to implement changes such as markup, content restructuring, knowledge hub improvements, and publishing workflows that increase AI trust and retrieval performance.
- Create scalable AEO playbooks and enablement materials for technical product managers, including templates for Q&A, reference blocks, and “LLM-friendly” technical summaries.
- Stay current with advances in LLM optimization, vector search, retrieval models, reranking, grounding/citations, and semantic discoverability; rapidly incorporate emerging best practices.
- Bachelor’s or Master’s degree in Data Science, Computer Science, Information Science, or related discipline.
- 1–3 years of experience in any of the following areas: ranking frameworks, forecasting models, search/retrieval optimization, information architecture, data/knowledge engineering, AI/ML implementation, technical documentation for complex systems.
- Knowledge of LLMs, prompt engineering, embeddings, and RAG architectures.
- Experience measuring and improving retrieval quality using metrics such as precision/recall, MRR, NDCG, and related evaluation methods.
- Strong skills in data analysis, scripting, and technical communication.
- Familiarity with Oracle AI Database, vector search, or comparable semantic retrieval technologies.
- Hands-on experience building or optimizing LLM-backed experiences, including content designed for grounded inference and citation-ready answers.
- Experience with neural networks, neural machine translation, or modern NLP architectures.
- Experience with relevance feedback loops, evaluation harnesses, and model quality monitoring in production settings.
- Background creating enterprise-grade knowledge bases, content hubs, or search/retrieval systems.
- Familiarity with tokenization
Apply for this role
Generate a tailored application kit with a matched cover letter, interview prep, and CV highlights — in under 60 seconds.
Apply Now →Generate Application KitFree account required — sign up in 30s