[Remote] Legal Quality Assurance Lead (QAL)
Note: The job is a remote job and is open to candidates in USA. SME Careers is a fast-growing AI Data Services company and subsidiary of SuperAnnotate, focused on delivering training data for leading AI companies. They are seeking a Legal Quality Assurance Lead (QAL) to oversee quality and consistency across legal AI training projects, reviewing AI-generated legal content and ensuring adherence to quality standards.
Responsibilities
- Quality monitoring: Spot-check legal items, identify quality issues, provide ongoing feedback through DMs, and escalate recurring or critical issues
- Legal review: Evaluate 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
- Trainer and QA communication: Update trainers and QAs on Discord about new item guidelines, project changes, workflow updates, quality expectations, and legal-review-specific standards
- Question handling: Respond to trainer/QA questions clearly and promptly, especially around legal reasoning, jurisdiction, citations, source quality, disclaimers, contract language, compliance interpretation, and rubric application
- Trainer/QA activation management: DM contributors who are inactive or not working, encourage activation, track follow-ups, and flag availability issues when needed
- Documentation: Create and maintain legal project documentation, including style guides, trackers, FAQs, quality notes, examples, honeypots, calibration tasks, and onboarding materials
- Onboarding and training: Schedule and run onboarding/training calls with trainers and QAs to explain project expectations, workflows, rubrics, quality standards, and legal-specific review requirements
- Quality alignment: Ensure all trainers and QAs apply legal-review guidelines consistently and understand updates as projects evolve
- Risk and safety review: Flag unsafe, misleading, overconfident, or jurisdictionally inappropriate legal outputs, especially where the content could be interpreted as personalized legal advice
- Process improvement: Identify recurring quality gaps, propose workflow improvements, and help build scalable QA processes for legal AI training projects
Skills
- 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
- Highly detail-oriented and organized, with the ability to maintain style guides, FAQs, trackers, onboarding materials, honeypots, calibration tasks, and other quality documentation
- 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
- Experience with AI training, data annotation, large language models, prompt/response evaluation, legal content QA, or rubric-based LLM evaluation
Company Overview