Associate Product Manager

Requirements

• J.D. from an accredited law school and a strong understanding of legal research methods, concepts, and their application within litigation workflows. Ability to dissect complex legal issues.
• 0-2 years of experience in a related field (associate attorney, law clerk, legal technology) or direct experience as an Associate Product Manager. Demonstrable aptitude for product thinking, requirements gathering, and translating needs into specifications, especially for evaluation.
• Strong, demonstrable interest in artificial intelligence, particularly generative AI. Eagerness and ability to quickly learn about AI tools, techniques, evaluation metrics, and data quality concepts. (Prior AI experience or basic programming is a plus, but not required).
• Good analytical and problem-solving skills with exceptional attention to detail. Ability to break down information and learn complex topics systematically. Comfortable asking questions to clarify ambiguity.
• Excellent written and verbal communication skills. Strong ability to collaborate effectively within a team environment and document information clearly.
• Bright, tech-savvy, highly organized, proactive learner with a strong sense of ownership and a drive to contribute to team success. Eager to learn and stay informed about technology trends.

• Legal Research Analysis:
  Assist in analyzing legal research and litigation workflows to understand user needs and how generative AI can assist with specific tasks (e.g., legal research, brief analysis, motion improvement). Help document evaluation criteria needed to meet the high standards of legal practice under the guidance of senior team members.
• Evaluation Specification & Support:
  Support the implementation of automated evaluation frameworks by developing clear functional specifications and tracking key metrics. Help assess factors like factual accuracy, legal soundness, citation validity, and relevance for specific features based on defined criteria.
• Data & Testing Assistance:
  Assist with the creation, organization, and curation of datasets needed for auto-evaluation. Support the coordination of automated testing, track execution results, document findings, and elevate potential issues for improvement.
• Requirements Documentation &
  
  Collaboration:
  
  Work closely with product managers, research scientists, engineers, and attorney editors to meticulously document requirements, user stories, and specifications for evaluation features. Facilitate clear communication within the development team.
• Technical Learning & Application:
  Develop a strong understanding of LLMs, generative AI evaluation techniques, and prompt engineering through active learning and on-the-job training. Assist in applying this knowledge to support the improvement of evaluation methods.
• Status Reporting:
  Assist in clearly communicating feature status, progress against requirements, evaluation results, and potential risks to the immediate team and stakeholders as directed.
• User & Market Research Support:
  Assist with user research activities (e.g., note-taking during interviews, survey data collation) and competitive analysis to help gather insights into user needs regarding AI content evaluation.