Neuroscience Quality Assurance Lead (QAL)
SME Careers • Remote
Education
Any
Type
hourly
Pay Rate
$90/task
Listed
21d ago
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About this Role
From the SME Careers listing
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.
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.
Your profile
- 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.
🌍 Geographic Availability This role is available to qualified experts globally. However, as a U.S.-based company, SME Careers complies with U.S. export control and sanctions regulations. Eligibility depends on your country of residence and any applicable trade restrictions. Some projects may have additional language or region-specific requirements.
💰 Pay Rate May Vary Based on Your Location Your actual compensation will be determined based on your location and local market conditions. See regional job listings for localized pay information, or the application process will clarify your specific rate.
If you're unsure whether you're eligible, proceed with the application—SME's screening will determine your jurisdiction status and applicable compensation.
Requirements
- AI Training
- Trainer Feedback
- Neuroscience
- Cognitive Science
- Brain Science
- LLM evaluation
- Cognitive Psychology
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.
Why This Role
Don't just label data as a Neuroscience Quality Assurance Lead (QAL), build a career. SME Careers is unique in the AI training space because it offers a transparent growth ladder. High performers in QA Testing aren't just kept in the queue; they are promoted to Quality Analysts and Project Managers. With weekly transparent payments via Deel and the freedom to work on your own schedule, this is built for modern experts who want long-term engagement.
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Frequently Asked Questions
Does SME Careers offer a path for advancement?
Yes. SME Careers is unusual among AI training platforms for this: it publishes an explicit career ladder, Data Trainer, then Quality Analyst, then QA Lead, then Project Manager. Consistent, high-quality work is what moves you up it.
How does SME Careers pay freelancers?
Weekly, through Deel. Deel also handles tax compliance, which is how SME Careers pays contractors across more than 40 countries without running its own international payroll.
Is SME Careers work ongoing or project-based?
Project-based. Assignment duration and availability vary by project, but consistently high-quality work earns priority access to whatever comes next.
What does task-based AI training work actually look like?
Practical, hands-on data work: recording short videos, categorizing images, rating text responses, or analyzing data. Tasks are designed to be short and distinct, typically 5 to 60 minutes each.
What does asynchronous AI training work mean in practice?
No set hours, no check-ins, no meetings. You log in when you want, pick up an available task, complete it, and submit; nobody is waiting on you in real time. That's different from remote employment, where you're expected online during business hours. The tradeoff: you're competing with others for available tasks, so an empty queue means there's simply nothing to do until more work is released.
Do task-based AI training roles require an interview?
Check the Job Facts panel above for this specific listing. Where no fact is confirmed yet: most of these roles skip a live interview and gate access through an automated assessment or qualification task instead, though some do add an interview step, so don't assume either way until you check the application flow.
What does QA Testing work look like for a Neuroscience Quality Assurance Lead (QAL)?
Tasks here are scoped to QA Testing, not generic labeling. As a Neuroscience Quality Assurance Lead (QAL), expect to draw on real domain judgment (evaluating outputs, correcting errors, or providing expert reasoning specific to QA Testing) rather than following a one-size-fits-all rubric. If you don't have hands-on QA Testing background, this is likely not the right listing to start with.
What happens when I click Apply on this listing?
You'll be taken to SME Careers's external site to complete your application there. This listing links through a referral, but the process is identical to applying directly; the link just routes you correctly. Create an account on their site and follow their onboarding steps.
Who is behind SME Careers?
SME Careers is the expert talent division of SuperAnnotate, an AI data infrastructure platform, connecting domain experts with high-level AI training projects.