AI Evaluation
Neuroscience Quality Assurance Lead (QAL)
Status: Open
- Pay
- $90/hr
- Location
- Remote — The United States
- Employment type
- Contractor
About this opportunity
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 SuperAnnotate, 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.
Required skills
- Trainer Feedback
- Scientific review
- Research Methods
- Neurobiology
- Cognitive Psychology
- LLM evaluation
- AI Training
- Brain Science
- Cognitive Science
- Neuroscience
What you'll do
- 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.