Neuroscience Quality Assurance Lead; QAL

Pay: up to $90/hour

In this hourly, remote contractor role, you will work as a Neuroscience / Cognitive Science Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across neuroscience and cognitive science AI training projects. You will review AI-generated neuroscience/cognitive science 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, conceptual precision, research literacy, experimental-method understanding, brain-behavior reasoning, statistical caution, ethical 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 neuroscience/cognitive science expertise, strong English communication skills, excellent attention to detail, structured communication, and the ability to manage quality workflows across remote expert 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 neuroscience/cognitive science quality leadership will directly help improve the world’s premier AI models by ensuring that scientific training data is accurate, evidence-aware, ethically appropriate, clearly explained, 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 neuroscience/cognitive science items, identify quality issues, provide ongoing feedback through DMs, and escalate recurring or critical issues.
• Scientific review:
  Evaluate AI-generated neuroscience/cognitive science explanations, research summaries, experimental interpretations, brain-behavior claims, cognitive theory applications, and step-by-step reasoning for accuracy and clarity.
• Trainer and QA communication:
  Update trainers and QAs on Discord about new item guidelines, project changes, workflow updates, quality expectations, and neuroscience/cognitive-science-specific review standards.
• Question handling:
  Respond to trainer/QA questions clearly and promptly, especially around neural mechanisms, cognition, experimental design, statistical interpretation, ethical boundaries, clinical caution, 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 neuroscience/cognitive science 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 neuroscience/cognitive-science-specific review requirements.
• Quality alignment:
  Ensure all trainers and QAs apply neuroscience/cognitive science review guidelines consistently and understand updates as projects evolve.
• Safety and ethics review:
  Flag pseudoscientific, overconfident, clinically misleading,…