Experience with AI training, data annotation, LLM evaluation, academic QA, ethics review, or rubric ... Evaluate AI-generated philosophy explanations, argument analyses, ethical reasoning, summaries of ...
Experience with AI training, data annotation, LLM evaluation, academic QA, ethics review, or rubric ... Evaluate AI-generated philosophy explanations, argument analyses, ethical reasoning, summaries of ...
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Quick apply
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Remote Data Annotation Analyst information
How does a Remote Data Annotation Analyst typically collaborate with team members and ensure consistent labeling standards?
What are Remote Data Annotation Analysts?
What is the difference between Remote Data Annotation Analyst vs Remote Data Labeler?
| Aspect | Remote Data Annotation Analyst | Remote Data Labeler |
|---|---|---|
| Required Credentials | Basic computer skills, attention to detail | Basic computer skills, attention to detail |
| Work Environment | Home-based, flexible hours | Home-based, flexible hours |
| Industry Usage | AI, machine learning, data science | AI, machine learning, data science |
| Job Focus | Analyzing and verifying labeled data, quality control | Labeling data, annotating images, text, or audio |
The main difference is that Remote Data Annotation Analysts focus on verifying and ensuring the quality of labeled data, often involving analysis and review, while Remote Data Labelers primarily perform the task of labeling or annotating raw data. Both roles are essential in AI development and share similar work environments and skill requirements, but their specific responsibilities differ in scope and focus.
What are the key skills and qualifications needed to thrive as a Remote Data Annotation Analyst, and why are they important?
Full-time
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Job description
Job Description
Job Title: Philosophy Quality Assurance Lead
Job Type: Contract
Location: Remote
About This RoleIn this hourly, remote contractor role, you will work as a Philosophy Quality Assurance Lead to oversee quality, consistency, and trainer performance across philosophy-focused AI training projects. You will review AI-generated philosophy 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 conceptual accuracy, argument structure, logical validity, philosophical context, interpretation quality, clarity, nuance, 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 philosophy 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 a fast-growing AI Data Services company delivering training data for many of the world’s largest AI companies and foundation-model labs. Your philosophy quality leadership will directly help improve the world’s premier AI models by ensuring that philosophy training data is conceptually precise, logically sound, well-contextualized, nuanced, 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.
Your Profile- Bachelor’s, Master’s, or PhD degree in Philosophy, Ethics, Logic, Political Theory, Humanities, Religious Studies, Classics, Cognitive Science, 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 philosophy research, teaching, writing, editing, academic review, ethics review, logic instruction, or related humanities workflows.
- Strong understanding of philosophical methods, logic, argument analysis, ethics, epistemology, metaphysics, philosophy of mind, political philosophy, history of philosophy, and conceptual clarification.
- Ability to evaluate philosophy content against detailed rubrics and identify issues such as strawman arguments, invalid inference, conceptual confusion, oversimplification, unsupported claims, false equivalence, or misrepresentation of philosophers’ views.
- Familiarity with areas such as formal logic, moral philosophy, applied ethics, analytic philosophy, continental philosophy, ancient philosophy, political philosophy, philosophy of science, or AI ethics is preferred.
- Experience leading or supporting remote teams of researchers, writers, reviewers, educators, annotators, 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, academic QA, ethics review, or rubric-based review is a strong plus.
- Quality monitoring: Spot-check philosophy items, identify quality issues, provide ongoing feedback through DMs, and escalate recurring or critical issues.
- Philosophy review: Evaluate AI-generated philosophy explanations, argument analyses, ethical reasoning, summaries of philosophers, logic problems, and conceptual comparisons for accuracy and rigor.
- Trainer and QA communication: Update trainers and QAs on Discord about new item guidelines, project changes, workflow updates, quality expectations, and philosophy-specific review standards.
- Question handling: Respond to trainer/QA questions clearly and promptly, especially around argument validity, conceptual distinctions, philosophical traditions, interpretation, ethics, logic, 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 philosophy 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 philosophy-specific review requirements.
- Quality alignment: Ensure all trainers and QAs apply philosophy-review guidelines consistently and understand updates as projects evolve.
- Reasoning and ethics review: Flag invalid, misleading, biased, overconfident, poorly argued, or ethically shallow philosophical content.
- Process improvement: Identify recurring quality gaps, propose workflow improvements, and help build scalable QA processes for philosophy AI training projects.