Neuroscience Quality Assurance Lead

Role overview

This is a remote contract position paid on an hourly basis. The role is focused on leading quality assurance for neuroscience and cognitive science AI training work, with responsibility for keeping outputs accurate, consistent, and aligned with project standards.

You will review AI-generated neuroscience and cognitive science content, assess trainer and QA performance, and provide clear written guidance to help contributors meet the required standards. The work involves checking for scientific accuracy, conceptual precision, research literacy, understanding of experimental methods, brain-behavior reasoning, statistical caution, ethical awareness, clarity, formatting, instruction adherence, and compliance with project-specific rubrics.

The organization is an AI data services company that supports major AI labs and foundation-model teams. Your contribution will help improve scientific training data so that it is accurate, evidence-aware, ethically sound, clearly explained, and better aligned with client needs.

There is currently no active project assigned to this role. If you are qualified, you may be contacted first when relevant opportunities become available, and you may also gain access to future work through the expert network.

Selection process

The hiring process includes an AI interview, a task tailored to the domain, and a conversation with a recruiter.

Responsibilities

• Monitor the quality of neuroscience and cognitive science items, identify recurring issues, share feedback directly, and escalate serious problems when needed.
• Review AI-generated explanations, research summaries, experimental interpretations, and reasoning steps for scientific accuracy and clarity.
• Keep trainers and QAs informed about guideline changes, workflow updates, quality expectations, and domain-specific review standards.
• Answer questions from trainers and QAs clearly and quickly, especially on neural mechanisms, cognition, study design, statistics, ethics, and rubric interpretation.
• Reach out to inactive contributors, encourage re-engagement, track follow-ups, and flag availability concerns.
• Build and maintain project documentation such as style guides, trackers, FAQs, calibration tasks, honeypots, examples, and onboarding materials.
• Plan and conduct onboarding and training sessions to explain workflows, rubrics, quality standards, and review expectations.
• Keep review practices aligned so trainers and QAs apply the same neuroscience and cognitive science standards as projects evolve.
• Identify pseudoscientific, exaggerated, clinically misleading, ethically problematic, or unsupported claims about the brain, behavior, cognition, or mental health.
• Suggest process improvements and help create scalable QA workflows for neuroscience and cognitive science AI training projects.

Requirements

• A 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 English communication skills for understanding guidelines, coordinating with teams, and writing precise feedback.
• At least 3 years of experience in neuroscience or cognitive science research, teaching, lab work, academic review, science communication, experimental design, data analysis, or similar scientific work.
• Solid understanding of neural systems, cognition, perception, attention, memory, learning, language, decision-making, neuroanatomy, neural signaling, research methods, and brain-behavior relationships.
• Ability to judge work against detailed rubrics and detect neuromyths, overclaiming, weak causal claims, incorrect interpretation of studies, misuse of terminology, pseudoscience, or misleading clinical implications.
• Preferred familiarity with EEG, fMRI, behavioral experiments, computational modeling, neuropsychological assessment, statistics, Python, R, MATLAB, cognitive tasks, or literature review methods.
• Strong preference for people who have led or supported remote teams of researchers, reviewers, educators, annotators, science writers, or QAs.
• Comfort using remote collaboration tools such as Discord, Google Sheets, Google Docs, trackers, dashboards, and project management platforms.
• High attention to detail and strong organization, including experience maintaining style guides, FAQs, trackers, onboarding resources, and documentation.
• Experience with AI training, data annotation, LLM evaluation, scientific QA, academic review, psychology or neuroscience content review, or rubric-based assessment is an added advantage.

Work style and additional notes

This role requires structured communication, close attention to detail, and the ability to manage quality workflows across distributed expert teams. You should be comfortable working in a fast-moving remote environment and be able to support consistency, documentation, and contributor activation across ongoing projects.