About this role
In this hourly, remote contractor role, you will work as a Medicine Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across medical AI training projects. You will review AI-generated medical content and trainer/QA work, evaluate output quality against project guidelines, provide precise written feedback, and ensure that all contributors follow the expected quality standards.
You will assess work for medical accuracy, clinical reasoning quality, guideline awareness, patient-safety awareness, terminology correctness, clarity, risk sensitivity, formatting, instruction-following, and adherence to project-specific rubrics. You will spot recurring quality issues, communicate updates to trainers and QAs, support onboarding, maintain documentation, and help activate contributors who are not working consistently. This role requires strong medical expertise, strong English communication skills, excellent attention to detail, structured communication, and the ability to manage quality workflows across remote medical-review teams.
This role is with SME Careers, a fast-growing AI Data Services company and subsidiary of SuperAnnotate, delivering training data for many of the world’s largest AI companies and foundation-model labs. Your medical quality leadership will directly help improve the world’s premier AI models by ensuring that medical training data is accurate, clinically sound, clearly explained, appropriately cautious, safety-aware, well-documented, and aligned with client expectations.
Selection process involves an AI interview, a domain-specific task, and an interview with a recruiter.
Important:
There is no immediate project for this role; however, if qualified, you will be among the first experts we reach out to when relevant opportunities arise. This will also provide you with access to future projects available through our expert network.
Requirements
- Medical degree such as MD, DO, MBBS, MBChB, or equivalent; advanced clinical, biomedical, nursing, pharmacy, or healthcare-related degrees may be considered depending on project requirements.
- Strong grasp of the English language to follow project guidelines, communicate with teams, and provide clear medical-review feedback in English.
- 3+ years of professional experience in medicine, clinical practice, medical research, medical education, clinical documentation, healthcare QA, medical writing, guideline review, or related workflows.
- Strong understanding of core medical topics such as clinical reasoning, differential diagnosis, pathophysiology, pharmacology, diagnostics, treatment principles, patient safety, evidence-based medicine, medical terminology, and healthcare communication.
- Ability to evaluate medical content against detailed rubrics and identify issues such as unsafe recommendations, hallucinated facts, missing caveats, incorrect clinical reasoning, overconfident diagnosis/treatment claims, inappropriate patient advice, or incomplete explanations.
- Familiarity with medical workflows or references such as clinical guidelines, diagnostic pathways, medication safety, chart review, case summaries, patient education materials, medical literature, and evidence-based review is preferred.
- Experience leading or supporting remote teams of trainers, annotators, reviewers, clinicians, medical writers, researchers, educators, or QAs is strongly preferred.
- 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, medical content QA, or rubric-based LLM evaluation is a strong plus.
Responsibilities
- Quality monitoring: Spot-check medical items, identify quality issues, provide ongoing feedback through DMs, and escalate recurring or critical issues.
- Medical review: Evaluate AI-generated medical explanations, clinical reasoning, patient-facing responses, case analyses, diagnostic discussions, treatment summaries, medication-related content, and health-education materials 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 medical-review-specific standards.
- Question handling: Respond to trainer/QA questions clearly and promptly, especially around clinical reasoning, terminology, patient-safety risks, medical claims, guideline interpretation, evidence quality, 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 medical 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 medicine-specific review requirements.
- Quality alignment: Ensure all trainers and QAs apply medical-review guidelines consistently and understand updates as projects evolve.
- Risk and safety review: Flag unsafe, misleading, overconfident, or clinically inappropriate medical outputs, especially where the content could be interpreted as personalized diagnosis, treatment, emergency guidance, medication instructions, or professional medical advice.
- Process improvement: Identify recurring quality gaps, propose workflow improvements, and help build scalable QA processes for medical AI training projects.
Skills
- Medicine
- LLM Evaluation
- Healthcare Documentation
- AI Training
- Patient Safety
- Medical QA
- Clinical Reasoning
- Trainer Feedback
- English
- Medical Review
Additional skills
- Quality Assurance (QA)
- Medical Quality Review
- Clinical Reasoning
- Differential Diagnosis
- Pathophysiology
- Pharmacology
- Diagnostics
- Treatment Planning
- Patient Safety
- Evidence-Based Medicine (EBM)
- Medical Terminology
- Clinical Guidelines Review
- Medication Safety
- Chart Review
- Clinical Documentation
- Medical Writing
- Medical Editing
- Medical Literature Review
- Peer Review
- Rubric-Based Evaluation
- Data Annotation
- LLM Evaluation
- AI Training Data QA
- Prompt Engineering
- Content Moderation
- Risk Assessment
- Root Cause Analysis (RCA)
- Team Leadership
- Project Management
- Google Sheets
