About this role
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 SuperAnnotate, 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.
Requirements
- Bachelor’s, Master’s, or PhD degree in Biology, Molecular Biology, Cell Biology, Genetics, Microbiology, Biochemistry, Ecology, Evolutionary Biology, Neuroscience, Physiology, or a closely related field.
- Strong grasp of the English language to follow project guidelines, communicate with teams, and provide clear scientific feedback in English.
- 3+ years of professional experience in biology research, laboratory work, teaching, scientific writing, technical review, quality control, biotechnology, life sciences, or related workflows.
- Strong understanding of core biology topics such as cell biology, molecular biology, genetics, evolution, ecology, physiology, microbiology, biochemistry, immunology, anatomy, developmental biology, and experimental design.
- Ability to evaluate biology content against detailed rubrics and identify issues such as incorrect assumptions, flawed experimental reasoning, inaccurate terminology, missing context, unsafe recommendations, hallucinated facts, incomplete explanations, or misleading scientific claims.
- Familiarity with common biology tools or workflows such as laboratory documentation, microscopy, PCR/qPCR, sequencing, gel electrophoresis, cell culture, ELISA, bioinformatics basics, statistical interpretation, safety data sheets, or scientific literature review is preferred.
- Experience leading or supporting remote teams of trainers, annotators, reviewers, researchers, technical writers, 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, scientific content QA, or rubric-based LLM evaluation is a strong plus.
Responsibilities
- Quality monitoring: Spot-check biology items, identify quality issues, provide ongoing feedback through DMs, and escalate 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.
Skills
- LLM Evaluation
- Molecular Biology
- Biology QA
- Biology
- AI Training
- Documentation
- Genetics
- Trainer Feedback
- Scientific Review
- Biological Reasoning
Additional skills
- Quality Assurance
- Scientific Quality Control
- Rubric-Based Evaluation
- Scientific Writing
- Technical Editing
- Peer Review
- Literature Review
- Experimental Design
- Data Annotation
- LLM Evaluation
- AI Training Data
- Prompt Evaluation
- Content Moderation
- Biology
- Molecular Biology
- Cell Biology
- Genetics
- Microbiology
- Biochemistry
- Immunology
- Physiology
- Bioinformatics
- Statistical Analysis
- Laboratory Documentation
- Microscopy
- PCR
- qPCR
- Sequencing
- Cell Culture
- ELISA
