Medicine QA Lead - Remote

YO IT Consulting; Delhi; Remote; INR 5,213,245 - 7,819,867; Part time

Job summary

YO IT Consulting is seeking a Medicine Quality Assurance Lead for a remote contractor role focusing on quality oversight in medical AI training projects. The successful candidate will assess AI-generated medical content, ensuring accuracy and clarity while managing workflows across remote teams.

Applicants should possess a medical degree and have at least 3 years of experience in clinical practice or healthcare QA. This role provides an opportunity to contribute significantly to AI developments in the healthcare sector.

Qualifications

• Medical degree such as MD, DO, MBBS, or equivalent required.
• 3+ years of professional experience in medicine or healthcare QA.
• Strong understanding of medical topics and guidelines.

Responsibilities

• Evaluate AI-generated medical content for accuracy and clarity.
• Provide ongoing feedback to trainers and QAs.
• Create and maintain quality documentation.

Skills

Medical expertise

Strong English communication

Attention to detail

Quality workflow management

Remote team leadership

Education

Medical degree (MD, DO, MBBS, etc.)

Advanced clinical or healthcare-related degrees

Tools

Google Docs

Discord

Job description

Job Description

Job Title: Medicine Quality Assurance Lead

Job Type: Contract

Location: Remote

About This Role

In this hourly, remote contractor role, you will work as a Medicine Quality Assurance Lead 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 a fast-growing AI Data Services company 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. 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.

Your Profile

• 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.

Key Responsibilities

• Quality monitoring: Spot-check medical items, identify quality issues, provide ongoing feedback through DMs, and elevate 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.