Job Title

Role- Director of AI Retrieval & Citation Systems CiteWorks Studio is hiring a Director of AI Retrieval & Citation Systems to lead pioneering research into how Large Language Models (LLMs) find, evaluate, and attribute information. This is a high-impact leadership role at the intersection of information retrieval (IR), source attribution, and Generative Engine Optimization (GEO). The Mission As Director, you will deconstruct the /"black box/" of AI retrieval. You will lead research exploring how platforms like ChatGPT, Claude, Gemini, and Perplexity select their /"Trusted Sources/" and how those choices dictate the visibility, trust, and authority of global brands. What is AI Retrieval & Citation Systems Research? It is the study of how generative systems retrieve knowledge and produce source-linked answers. In the modern LLM landscape, this includes: Retrieval Mechanics: How AI identifies internal vs. external data. Source Selection: The logic behind which domains are deemed /"Trusted./" Citation Behavior: How and where citations appear within AI-generated answers. Source Attribution: How attribution signals vary across different models and platforms. Key Responsibilities Lead Research Initiatives: Oversee deep dives into LLM retrieval pathways and generative search benchmarking. Analyze Citation Patterns: Build frameworks to map how often specific publishers and organizations are cited across ChatGPT, Claude, and Gemini. Map Trusted-Source Selection: Identify the recurring patterns that lead to certain domains becoming the /"default/" authority for AI models. Cross-Model Benchmarking: Compare retrieval consistency and attribution differences across proprietary and open-source models. Collaborate with ML Teams: Work with data and machine learning engineers to build scalable systems that capture and quantify citation behavior at scale. Publish & Influence: Drive the industry narrative by publishing research on AI citation intelligence and source attribution. Research Areas You Will Explore LLM Retrieval Systems: Synthesizing info across RAG-driven search systems. Citation Intelligence: Analyzing the frequency, recurrence, and variance of brand mentions. Source Pathways: Studying how attribution signals affect the final generated response. Trusted Reference Formation: Exploring how brands can consistently appear as trusted references in generative search results. Qualifications Required: 8+ years in Information Retrieval, Machine Learning, AI Systems, or Search Infrastructure. Expertise in LLMs: Deep understanding of how retrieval-augmented generation (RAG) and source attribution function. Leadership: Proven experience leading technical or research-focused teams in complex data environments. Communication: The ability to translate deep technical retrieval findings into practical strategic frameworks. Preferred: Direct experience with GEO (Generative Engine Optimization) or semantic search. Background in analyzing source authority and entity relationship systems. Familiarity with cross-model behavior analysis (OpenAI vs. Anthropic vs. Google). Why Join CiteWorks Studio? We are not just observing the AI revolution; we are mapping its architecture. This role offers the chance to define how organizations understand AI Citation Strategy and AI Share of Voice . If you are obsessed with the mechanics of how AI determines /"truth/" and /"authority,/" this is your frontier. Key Terminology at CiteWorks AI Citation Intelligence: The analysis of source frequency inside AI answers. Retrieval System: The engine identifying relevant data for synthesis. Generative Search: Synthesizing answers instead of returning ranked links. Source Attribution: Connecting a generated answer back to its informing source.