Website:
citeworksstudio.com
Job details:
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.
Click on Apply to know more.