Neuroscience Quality Assurance Lead (QAL)

Key Responsibilities

• 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, ethically problematic, or unsupported claims about the brain, cognition, behavior, or mental health.
• Process improvement: Identify recurring quality gaps, propose workflow improvements, and help build scalable QA processes for neuroscience/cognitive science AI training projects.

Requirements

• Bachelor’s, Master’s, PhD, MD/PhD, or equivalent professional background in Neuroscience, Cognitive Science, Psychology, Neurobiology, Cognitive Psychology, Computational Neuroscience, Neurology-adjacent research, Biology, Biomedical Sciences, or a closely related field.
• Strong grasp of the English language to follow project guidelines, communicate with teams, and provide clear written feedback.
• 3+ years of experience in neuroscience/cognitive science research, teaching, laboratory work, academic review, science communication, experimental design, data analysis, or related scientific workflows.
• Strong understanding of neural systems, cognition, perception, attention, memory, learning, language, decision-making, neuroanatomy, neural signaling, research methods, and brain-behavior relationships.
• Ability to evaluate neuroscience/cognitive science content against detailed rubrics and identify issues such as neuromyths, overclaiming, unsupported causal conclusions, flawed study interpretation, incorrect terminology, pseudoscience, or misleading clinical implications.
• Familiarity with tools or methods such as EEG, fMRI, behavioral experiments, computational modeling, neuropsychological assessment, statistics, Python/R/MATLAB, cognitive tasks, or literature review is preferred.
• Experience leading or supporting remote teams of researchers, reviewers, educators, annotators, science writers, 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, calibration tasks, and documentation.
• Experience with AI training, data annotation, LLM evaluation, scientific QA, academic review, psychology/neuroscience content review, or rubric-based review is a strong plus.