Legal Knowledge Engineer - Litigation

About the role

The Legal Knowledge Engineer - Litigation is an integral part of Latham’s Legal Innovation team. This role will develop and curate the knowledge foundations that power AI-enabled legal workflows, and create and maintain evaluation datasets, test cases, and quality benchmarks for AI-powered tools to support systematic assessment of output accuracy and consistency.

This role is located in either our New York, Boston, or Silicon Valley offices. Please note that this role may be eligible for a flexible working schedule that allows for a hybrid and in-office presence.

Responsibilities

• Developing and implementing quality assurance protocols and evaluation frameworks for AI outputs, establishing standards for accuracy, completeness, and alignment with attorney expectations
• Translating transactional practice requirements into structured knowledge repositories, prompt templates, and context specifications that optimize AI system inputs for high-quality outputs
• Analyzing AI output quality across workflows by identifying patterns, gaps, and opportunities to improve context, inputs, and evaluation criteria
• Monitoring and reporting on quality metrics, accuracy measures, and evaluation results for AI-powered workflows, using data-driven insights to guide knowledge asset refinement and context optimization
• Conducting research to identify best practices for knowledge curation, context management, and AI evaluation methodologies, while staying current with advances in retrieval-augmented generation and legal AI applications
• Protecting and maintaining any highly sensitive, confidential, privileged, financial, and/or proprietary information that Latham & Watkins retains

Qualifications

• Possess strong expertise in knowledge engineering and AI systems, with the ability to curate and structure information to optimize AI inputs and outputs for accuracy, consistency, and alignment with legal practice standards
• Demonstrate a solid understanding of transactional legal practice, including mergers and acquisitions (M&A), finance, and capital markets workflows, with the ability to translate practice expertise into structured knowledge assets and evaluation criteria
• Exhibit strong knowledge curation capabilities, including developing reference libraries, context repositories, evaluation datasets, and quality assurance frameworks for AI-powered transactional tools