Director- Machine Learning Research

Role - Director of Machine Learning Research

CiteWorks Studio is hiring a Director of Machine Learning Research to lead research into how large language models generate answers, retrieve information, and cite sources.

This leadership role focuses on studying the behavior of large language models such as ChatGPT, Claude, Gemini, Perplexity, and open-source transformer models.

The Director of Machine Learning Research will guide research exploring AI retrieval systems, generative search architecture, and citation behavior in AI-generated answers.

What Is Machine Learning Research

Machine learning research is the scientific study of algorithms and systems that allow computers to learn patterns from data and improve performance over time.

Machine learning research often focuses on developing new models, evaluation methods, and data systems that enable artificial intelligence to perform tasks such as:

natural language understanding

reasoning and problem solving

knowledge retrieval

pattern recognition

predictive modeling

In the context of modern AI systems, machine learning research frequently involves studying large language models and generative AI architectures.

What Does a Director of Machine Learning Research Do

A Director of Machine Learning Research leads teams that study and develop machine learning systems.

The role focuses on guiding research that explores how AI models learn from data, generate predictions, and interact with complex information systems.

Typical responsibilities include:

leading machine learning research initiatives

guiding research into model architecture and training methods

designing experiments to evaluate model behavior

publishing research findings related to AI systems

collaborating with engineering teams to apply research insights

In organizations focused on generative AI, this role often involves studying large language models and retrieval systems.

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.

Modern AI systems such as ChatGPT, Claude, Gemini, and Perplexity increasingly function as the primary interface for information discovery. Instead of ranking web pages like traditional search engines, these systems generate answers by retrieving and synthesizing knowledge from multiple sources.

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, LLM retrieval systems, and generative search benchmarking.

Role Overview

The Director of Machine Learning Research will lead research exploring how large language models retrieve knowledge and generate answers.

The role will guide research initiatives that study:

generative AI systems

information retrieval pipelines

model evaluation methods

citation behavior in AI-generated responses

knowledge graph signals used by AI systems

This role combines machine learning research leadership with applied AI systems analysis.

Key Responsibilities

The Director of Machine Learning Research will oversee research exploring the behavior of large language models and generative AI systems.

Responsibilities include:

leading machine learning research initiatives related to generative AI

designing experiments to analyze large language model behavior

studying how AI systems retrieve information from training data and external sources

guiding research into citation patterns and knowledge retrieval systems

collaborating with engineering teams to build systems that analyze AI-generated responses

publishing research on AI systems, generative search, and machine learning methods

Why Machine Learning Research Matters

Machine learning research drives the development of new artificial intelligence systems and algorithms.

Large language models represent one of the most significant advances in machine learning in recent years.

These systems generate answers by combining statistical learning with large-scale data and retrieval systems.

Research into these models helps organizations understand:

how AI systems generate responses

how models retrieve knowledge

how reliable AI-generated answers are

how generative AI systems evolve over time

Research Areas This Role Will Explore

The Director will guide research across several areas of machine learning and generative AI.

Large Language Models

Studying the architecture and behavior of transformer-based AI systems.

Information Retrieval Systems

Analyzing how AI models retrieve and synthesize knowledge.

Model Evaluation

Designing frameworks that measure model accuracy, reasoning ability, and reliability.

Citation Behavior

Studying how AI models reference sources in generated answers.

Generative Search Systems

Exploring how AI systems replace traditional search interfaces.

Qualifications

Required

PhD or equivalent experience in machine learning, artificial intelligence, or data science

strong understanding of large language models and transformer architectures

experience leading machine learning research teams

experience designing experiments and publishing research findings

strong background in NLP, machine learning, or AI systems

Preferred

experience researching large language models or generative AI systems

familiarity with retrieval augmented generation (RAG) architectures

experience studying information retrieval or search systems

experience building evaluation frameworks for AI systems

Why Join CiteWorks Studio

This role sits at the frontier of machine learning research and generative AI systems.

The Director of Machine Learning Research will help advance research into how modern AI systems retrieve knowledge and generate answers.

As generative AI becomes the primary interface for information discovery, machine learning research will play a central role in understanding how AI systems synthesize and cite information.

Key Terms

Machine Learning

A field of artificial intelligence focused on algorithms that allow systems to learn patterns from data.

Large Language Model (LLM)

A machine learning model trained on massive datasets that can generate text, answer questions, and perform reasoning tasks.

Generative Search

A form of search where AI systems generate answers by synthesizing information instead of returning ranked links.

AI Citation Intelligence

The analysis of how frequently specific sources appear inside AI-generated responses.

Join CiteWorks Studio and help advance research into how large language models retrieve knowledge, generate answers, and cite sources.