[ CAREER OPENING ]
Director of AI Retrieval & Citation Systems
CiteWorks Studio is hiring a Director of AI Retrieval & Citation Systems to lead research into how large language models retrieve information, generate answers, and select trusted sources across generative search systems.
This leadership role focuses on studying how AI retrieval systems, citation systems, source selection mechanisms, and source attribution patterns shape AI-generated answers across platforms such as ChatGPT, Claude, Gemini, Perplexity, and open-source large language models.
The Director of AI Retrieval & Citation Systems will guide research exploring LLM retrieval systems, citation behavior, trusted sources, and the source pathways that influence AI-generated answers.
[ OVERVIEW ]
What Is AI Retrieval & Citation Systems Research?
AI retrieval and citation systems research is the study of how large language models and generative search systems retrieve knowledge, evaluate trusted sources, and produce AI-generated answers that reference external information.
For modern large language models, this often includes understanding:
- how AI systems retrieve information from internal and external sources
- how trusted sources are selected during answer generation
- how citations appear inside AI-generated answers
- how retrieval pathways differ across generative search systems
- how source attribution patterns influence visibility, trust, and authority
This field helps explain why certain sources, domains, publishers, and organizations appear repeatedly inside AI-generated answers.
[ THE ROLE ]
What Does a Director of AI Retrieval & Citation Systems Do?
A Director of AI Retrieval & Citation Systems leads research and analysis focused on how AI systems retrieve knowledge, evaluate trusted sources, and generate source-linked answers.
The role focuses on studying how large language models and generative search systems:
- retrieve knowledge across multiple sources
- determine which trusted sources are most relevant
- decide which sources to cite inside AI-generated answers
- display source attribution across different answer formats
- vary retrieval and citation behavior across models and platforms
The Director works at the intersection of information retrieval, source attribution, generative search systems, and AI citation intelligence.
[ ABOUT US ]
About CiteWorks Studio
CiteWorks Studio is an AI research and generative engine optimization (GEO) firm focused on understanding how large language models retrieve and cite information. The company’s current role architecture consistently emphasizes large language models, trusted sources, AI-generated answers, knowledge graphs, AI citation intelligence, and generative search systems.
Modern AI systems such as ChatGPT, Gemini, Claude, and Perplexity increasingly function as the primary interface for information discovery. Instead of ranking links like traditional search engines, these systems generate answers by retrieving and synthesizing knowledge from multiple trusted sources. Existing CiteWorks materials frame this work around how AI systems determine trusted sources, how citation patterns appear inside AI-generated answers, and how knowledge graphs influence model responses.
CiteWorks Studio studies this transformation and helps organizations understand:
- how AI systems determine trusted sources
- how citation patterns appear inside AI-generated answers
- how knowledge graphs influence model responses
- how organizations become trusted references in generative search systems
Our research focuses on AI citation intelligence, generative search benchmarking, LLM retrieval systems, and trusted-source analysis.
[ ROLE OVERVIEW ]
Role Overview
The Director of AI Retrieval & Citation Systems will lead research exploring how large language models retrieve information, evaluate trusted sources, and generate AI-generated answers across generative search systems.
The role will guide initiatives that study:
- LLM retrieval systems
- citation systems inside AI-generated answers
- source attribution frameworks
- trusted-source selection patterns
- retrieval consistency across prompts and models
- why certain publishers and organizations become recurring trusted sources
This role combines retrieval systems research, citation intelligence, and applied analysis of AI-generated answers.
[ RESPONSIBILITIES ]
Key Responsibilities
The Director of AI Retrieval & Citation Systems will oversee research into how modern AI systems retrieve knowledge and generate AI-generated answers using trusted sources.
Responsibilities include:
- leading research into LLM retrieval systems and generative search systems
- analyzing how large language models retrieve information across prompts, topics, and domains
- studying how trusted sources are selected and cited inside AI-generated answers
- building frameworks that map citation patterns across ChatGPT, Claude, Gemini, Perplexity, and other large language models
- identifying retrieval patterns that influence which organizations become trusted references
- analyzing source attribution differences across models, prompts, and generative search systems
- collaborating with data and machine learning teams to build systems that capture retrieval behavior and citation behavior at scale
- publishing research on AI retrieval systems, trusted sources, source attribution, and AI citation intelligence
[ WHY IT MATTERS ]
Why AI Retrieval & Citation Systems Matter
Large language models do not simply predict words in isolation.
They generate AI-generated answers by combining learned patterns with retrieval behavior, source selection, and citation systems.
As generative search systems become the primary interface for information discovery, organizations need to understand:
- how AI systems retrieve knowledge
- why some trusted sources are selected more often than others
- how citations appear inside AI-generated answers
- how retrieval patterns shape visibility and authority
- why some organizations become recurring trusted references
AI retrieval and citation systems research helps explain how large language models determine trusted sources and shape AI-generated answers at scale.
[ RESEARCH AREAS ]
Research Areas This Role Will Explore
The Director will guide research across several areas of retrieval systems and citation systems.
LLM Retrieval Systems
Studying how large language models retrieve and synthesize information across generative search systems.
Citation Behavior
Analyzing how citations appear, recur, and vary inside AI-generated answers.
Trusted Sources
Researching how AI systems determine which publishers, domains, and references become trusted sources.
Source Attribution
Studying how source pathways and attribution signals affect AI-generated answers.
Cross-Model Retrieval Patterns
Comparing retrieval behavior and citation behavior across ChatGPT, Gemini, Claude, Perplexity, and open-source large language models.
Trusted Reference Formation
Exploring how organizations become trusted references that appear repeatedly in AI-generated answers.
[ QUALIFICATIONS ]
Qualifications
Required
- 8+ years experience in information retrieval, machine learning, AI systems, data science, or search infrastructure
- strong understanding of large language models, retrieval systems, and source attribution
- experience studying or building information retrieval systems, citation systems, or search-related data systems
- experience leading research or technical teams focused on complex AI systems
- ability to translate technical findings about retrieval systems and trusted sources into practical research frameworks
Preferred
- experience working with large language models or generative search systems
- familiarity with retrieval augmented generation (RAG) systems
- experience analyzing AI-generated answers, citation patterns, or trusted-source behavior
- background in information retrieval, semantic search, or source attribution systems
- experience comparing behavior across multiple large language models
[ WHY JOIN ]
Why Join CiteWorks Studio
This role sits at the frontier of AI search research, LLM retrieval systems, and AI citation intelligence.
The Director of AI Retrieval & Citation Systems will help advance research into how modern large language models retrieve information, determine trusted sources, and generate AI-generated answers across generative search systems.
As generative search systems become the primary interface for information discovery, organizations will increasingly need to understand how large language models retrieve knowledge, select trusted sources, and shape authority through AI-generated answers. That framing is tightly aligned with the company’s existing CAIO, AI Data, and ML Research roles.
[ KEY TERMS ]
Key Terms
Large Language Model (LLM)
A machine learning model trained on massive datasets that can generate text, answer questions, and perform reasoning tasks.
Retrieval System
A system that identifies and retrieves relevant information used to generate answers or support reasoning.
Trusted Sources
Sources that AI systems appear to rely on repeatedly when generating answers and citations.
AI Citation Intelligence
The analysis of how frequently specific sources appear inside AI-generated answers.
Generative Search
A form of search where AI systems generate answers by synthesizing information instead of returning ranked links.
Source Attribution
The process of connecting an AI-generated answer to the sources that informed it.
[ APPLY ]
Apply
If you are passionate about large language models, trusted sources, AI-generated answers, AI retrieval systems, AI citation intelligence, and generative search systems, we would love to hear from you.
Join CiteWorks Studio and help advance research into how large language models retrieve information, determine trusted sources, generate AI-generated answers, and shape which organizations become cited, positioned, and recommended across generative search systems.
Apply for this role
Send your resume, work samples, or portfolio along with a note on why you'd be the perfect fit. Include “Director of AI Retrieval & Citation Systems” in your subject line.
hr@citeworksstudio.com