Overview
Our client, a leading national law firm, is seeking an AI / Machine Learning Research Engineer to help advance the firm’s use of artificial intelligence across legal research, document analysis, knowledge management, and client service delivery. This role will focus on researching, designing, prototyping, and optimizing AI-driven solutions—particularly Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems—to support attorneys and legal professionals with accurate, efficient, and cost-effective insights.
The ideal candidate combines strong technical expertise with an understanding of data governance, legal workflows, and the importance of accuracy, explainability, and confidentiality in the legal industry.

Key Responsibilities
AI & LLM Research and Development

• Conduct applied research on the use of Large Language Models (LLMs) for legal document analysis, contract review, legal research, and other law firm use cases.
• Evaluate emerging AI technologies, frameworks, and methodologies relevant to the legal industry, including advances in natural language processing (NLP), embeddings, and generative AI.
• Explore opportunities for developing custom machine learning and deep learning models tailored to the firm’s proprietary data, legal practice areas, and business objectives.

Retrieval-Augmented Generation (RAG) & Information Retrieval

• Design, build, and test Retrieval-Augmented Generation (RAG) pipelines using various:
• Text preprocessing and chunking strategies
• Embedding models
• Vector databases
• Retriever and ranking strategies

• Identify optimal system configurations to improve answer relevance, accuracy, and response time while reducing compute and query costs.
• Analyze performance metrics to continuously improve retrieval precision and recall for legal content. Data Engineering & Data Quality
  • Gather, clean, normalize, and preprocess structured and unstructured data used for AI and machine learning initiatives.
  • Apply machine learning techniques to improve data quality, reduce noise, and enhance training data reliability.
  • Ensure data preparation processes comply with legal, ethical, and confidentiality standards expected in a law firm environment.
  Machine Learning & Advanced Analytics
  • Select appropriate machine learning algorithms based on business and legal use cases.
  • Tune hyperparameters, validate models, and ensure solutions meet defined performance, accuracy, and reliability criteria.
  • Identify patterns, trends, and relationships in data sets to uncover actionable insights for attorneys and business teams.
  • Develop predictive models and algorithms to forecast outcomes, support decision-making, and enhance operational efficiency.
  Collaboration & Stakeholder Engagement
  • Partner closely with database administrators, architects, software developers, legal professionals, and business stakeholders to understand requirements and translate them into effective AI solutions.
  • Communicate technical concepts and recommendations clearly to both technical and non-technical audiences, including senior leadership.
  • Support cross-functional teams in integrating AI solutions into existing systems and workflows.
  Documentation, Training & Knowledge Sharing
  • Create and maintain comprehensive documentation for AI models, data pipelines, methodologies, and system architectures.
  • Organize and facilitate training sessions, meetings, and presentations to promote AI literacy and knowledge transfer across the firm.
  • Serve as a thought partner on AI strategy, helping teams understand capabilities, limitations, and responsible use of AI technologies.
  Qualifications
  Required
  • Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, Artificial Intelligence, or a related field.
  • Hands-on experience with machine learning, deep learning, or NLP projects.
  • Experience building or experimenting with LLM-based systems, including RAG architectures.
  • Strong programming skills in Python and familiarity with common ML/AI libraries and frameworks.
  • Experience with data preprocessing, feature engineering, and model evaluation.
  • Strong analytical, problem-solving, and communication skills.
  Preferred
  • Experience working with legal, regulatory, or highly confidential data sets.
  • Familiarity with vector databases, embedding models, and modern NLP tooling.
  • Understanding of data governance, privacy, and compliance considerations in professional services environments.
  • Prior experience in a law firm, consulting firm, or regulated industry is a plus.