Biology Quality Assurance Lead; QAL

Pay: up to $75/hour

In this hourly, remote contractor role, you will work as a Biology Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across biology AI training projects. You will review AI-generated biology 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 scientific accuracy, biological reasoning, terminology correctness, experimental logic, unit consistency, safety awareness, clarity, 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 biology expertise, strong English communication skills, excellent attention to detail, structured communication, and the ability to manage quality workflows across remote technical teams.

This role is with SME Careers, a fast-growing AI Data Services company and subsidiary of Super Annotate, delivering training data for many of the world’s largest AI companies and foundation-model labs. Your biology quality leadership will directly help improve the world’s premier AI models by ensuring that biology training data is accurate, logically sound, clearly explained, 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.

• Quality monitoring:
  Spot-check biology items, identify quality issues, provide ongoing feedback through DMs, and elevate recurring or critical issues.
• Scientific review:
  Evaluate AI-generated biology explanations, biological mechanisms, experimental reasoning, genetics problems, diagrams/descriptions, terminology, and problem-solving steps for correctness and clarity.
• Trainer and QA communication:
  Update trainers and QAs on Discord about new item guidelines, project changes, workflow updates, quality expectations, and biology-specific review standards.
• Question handling:
  Respond to trainer/QA questions clearly and promptly, especially around biological reasoning, terminology, experimental design, methods, safety concerns, scientific claims, and rubric interpretation.
• 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 biology 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 biology-specific review requirements.
• Quality alignment:
  Ensure all trainers and QAs apply biology guidelines consistently and understand updates as projects evolve.
• Risk and safety review:
  Flag unsafe, misleading, or overconfident biology recommendations, especially where lab procedures, biological samples, pathogens, genetic engineering, health claims, environmental impact, or biosafety may be affected.
• Process improvement:
  Identify recurring quality gaps, propose workflow improvements, and help build scalable QA processes for biology AI training projects.