Philosophy Quality Assurance Lead (QAL)
Up to $65/hr as listed
- Listed pay
- Up to $65/hr as listed
- Field
- Writing
- Languages
- English
- Where
- United States
- Type
- Contract, remote
- Posted on SME Careers
- First seen here
- 9 October 2026
Summary
In this hourly, remote contractor role, you will work as a Philosophy Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across philosophy-focused AI training...
From the SME Careers listing
In this hourly, remote contractor role, you will work as a Philosophy Quality Assurance Lead (QAL) 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 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 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.
Responsibilities
- 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.
Requirements
- 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.
Text above is the platform's own listing, shown as published. Check the details on SME Careers before applying.
Prepare for the assessment
Practice questions and what each platform says about its screening for this kind of role:
Terms in this listing
- Guidelines
- The project's written instructions that define how to do a task and how to judge answers. On most projects they take precedence over personal preference.
- Instruction following
- Whether a model did exactly what was asked, including constraints such as length, format, language or things to avoid.
- Rubric
- A written list of criteria and scores used to judge a response, for example accuracy, instruction following and tone, each with clear pass or fail descriptions.
- AI interview
- A screening interview run by an AI system instead of a person, usually spoken or on video, with questions about your background and skills.
- Calibration
- Exercises where raters do the same tasks and compare results with the expected answers, so everyone applies the guidelines the same way.
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