Biology Quality Assurance Lead

As a Biology Quality Assurance Lead, you will review AI-generated biology content and quality assurance work across remote biology training projects. You will assess scientific accuracy, biological reasoning, terminology, experimental logic, units, safety awareness, clarity, formatting, instruction-following, and rubric adherence.

You will also give precise written feedback, help trainers and quality reviewers understand workflow changes, maintain project documentation, support onboarding, and address recurring quality issues across distributed teams.

• Review biology explanations, biological mechanisms, genetics problems, diagrams, and problem-solving steps for correctness and clarity.
• Spot-check contributor work, identify recurring quality issues, and escalate critical concerns.
• Explain project updates, guidelines, and biology-specific review standards to trainers and quality reviewers.
• Answer questions about biological reasoning, experimental design, methods, safety concerns, scientific claims, and rubric interpretation.
• Create and maintain trackers, examples, honeypots, calibration tasks, style guides, FAQs, and onboarding materials.
• Flag unsafe, misleading, or overconfident recommendations involving lab procedures, biological samples, pathogens, genetic engineering, health claims, or biosafety.
• Support onboarding, training calls, activation follow-up, and process improvements for scalable quality assurance workflows.

What it pays and takes

This is a remote contract role for someone with professional biology experience and strong scientific communication skills. The work is focused on reviewing and improving AI-generated biology content, so you must be comfortable judging both scientific detail and the quality of written reasoning.

• Pay: $75 per hour.
• Work setup: Remote within the United States.
• Workload: 20+ hours per week.
• Engagement: Contract and part-time.
• Language: Strong English communication skills are required.
• Education: A degree in biology, molecular biology, cell biology, genetics, microbiology, biochemistry, ecology, evolutionary biology, neuroscience, physiology, or a closely related field.
• Experience: At least 3 years in biology research, laboratory work, teaching, scientific writing, technical review, quality control, biotechnology, life sciences, or a related workflow.
• Knowledge: Strong understanding of cell biology, molecular biology, genetics, evolution, ecology, physiology, microbiology, biochemistry, immunology, anatomy, developmental biology, and experimental design.
• Review ability: Identify incorrect assumptions, flawed reasoning, inaccurate terminology, missing context, unsafe recommendations, hallucinated facts, incomplete explanations, and misleading scientific claims against detailed rubrics.
• Preferred experience: AI training, data annotation, large language models, prompt or response evaluation, scientific content quality assurance, or rubric-based language model evaluation.

Helpful background

Experience with laboratory documentation, scientific literature review, or common biology methods will help you evaluate content accurately. You should also be organized and comfortable supporting remote teams and maintaining detailed project materials.

• Familiarity with microscopy, PCR or qPCR, sequencing, gel electrophoresis, cell culture, ELISA, bioinformatics basics, statistical interpretation, or safety data sheets.
• Experience supporting remote teams of trainers, annotators, reviewers, researchers, technical writers, educators, or quality assurance specialists.
• Comfort using Discord, Google Sheets, Google Docs, trackers, dashboards, and project management systems.
• Ability to maintain style guides, FAQs, trackers, onboarding materials, honeypots, and calibration tasks.
• Highly organized, detail-oriented, and able to communicate clear scientific feedback.

How it works

Apply on OpenTrain with your resume, then complete the application on the hiring site.

About AI training work

AI training work is the human side of building artificial intelligence. People review model responses and prepare examples so AI systems can give more accurate, safe, and useful answers, and experienced specialists are paid for applying their subject knowledge to this review.