Neuroscience Quality Assurance Lead (QAL)

Skills

About this role

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.

Cognitive Science, Neurobiology, Research Methods

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-specific review standards.
• Question handling: Respond to trainer/QA questions clearly and promptly, especially around neural mechanisms, cognition, experimental design, statistical interpretation, and ethical boundaries.
• Trainer/QA activation management: DM contributors who are inactive or not working, encourage activation, track follow-ups, and flag availability issues when needed.

Qualifications

• Bachelor's, Master's, PhD, MD/PhD, or equivalent professional background in Neuroscience, Cognitive Science, Psychology, Neurobiology, Cognitive Psychology, Computational Neuroscience, 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, or pseudoscience.