Legal QA Lead - Remote
About the role
This is an hourly, remote contractor role as a Legal Quality Assurance Lead overseeing quality, consistency, and trainer performance across legal AI training projects.
Responsibilities
Review AI-generated legal content and trainer/QA work, evaluating output quality against project guidelines and providing precise written feedback.
Evaluate legal content for legal reasoning quality, issue spotting, jurisdictional awareness, citation and source handling, clarity, risk awareness, formatting, instruction-following, and adherence to project-specific rubrics.
Assess work for recurring quality issues, communicate updates to trainers and QAs, support onboarding, maintain documentation, and help activate contributors who are not working consistently.
Provide quality monitoring by spot-checking legal items, identifying quality issues, providing ongoing feedback through direct messages (DMs), and escalating recurring or critical issues.
Perform legal review by evaluating AI-generated legal explanations, legal research responses, contract analyses, policy interpretations, case summaries, compliance guidance, and issue-spotting workflows for accuracy, clarity, and appropriate caution.
Communicate with trainers and QAs on Discord about new item guidelines, project changes, workflow updates, quality expectations, and legal-review-specific standards.
Handle trainer/QA questions clearly and promptly, especially around legal reasoning, jurisdiction, citations, source quality, disclaimers, contract language, compliance interpretation, and rubric application.
Manage trainer/QA activation by DMing inactive contributors, encouraging activation, tracking follow-ups, and flagging availability issues when needed.
Create and maintain legal project documentation, including style guides, trackers, FAQs, quality notes, examples, honeypots, calibration tasks, and onboarding materials.
Schedule and run onboarding/training calls with trainers and QAs to explain project expectations, workflows, rubrics, quality standards, and legal-specific review requirements.
Ensure consistent application of legal-review guidelines by trainers and QAs and help build scalable QA processes for legal AI training projects.
Identify and flag unsafe, misleading, overconfident, or jurisdictionally inappropriate legal outputs, especially where the content could be interpreted as personalized legal advice.
Requirements
Bachelor’s degree, JD, LLB, LLM, or equivalent degree in Law, Legal Studies, Paralegal Studies, Compliance, Public Policy, or a closely related field.
Strong grasp of the English language to follow project guidelines, communicate with teams, and provide clear legal-review feedback in English.
3+ years of professional experience in legal practice, legal research, contract review, compliance, regulatory analysis, paralegal work, legal operations, legal writing, legal education, or related workflows.
Strong understanding of core legal concepts such as legal reasoning, issue spotting, contract interpretation, legal research, statutory interpretation, case analysis, compliance, risk assessment, and legal writing standards.
Ability to evaluate legal content against detailed rubrics and identify issues such as unsupported conclusions, hallucinated citations, jurisdictional mismatch, overconfident legal advice, missing caveats, incorrect legal standards, incomplete reasoning, or unclear explanations.
Familiarity with common legal workflows or tools such as contract review, legal memoranda, case summaries, regulatory guidance, citation checking, legal databases, document review, compliance checklists, or policy analysis.
Experience leading or supporting remote teams of trainers, annotators, reviewers, lawyers, paralegals, legal writers, researchers, or QAs.
Comfortable working in fast-moving remote environments using tools such as Discord, Google Sheets, Google Docs, trackers, dashboards, and project management systems.
Highly detail-oriented and organized, with the ability to maintain style guides, FAQs, trackers, onboarding materials, honeypots, calibration tasks, and other quality documentation.
Experience with AI training, data annotation, large language models, prompt/response evaluation, legal content QA, or rubric-based LLM evaluation is a strong plus.