AI Humanities Tutor: Scholarly Data Curation

xAI; United Kingdom; Remote; GBP 36,000 - 77,000; Full time

Benefits offered by this job

Health insurance

401(k) plan

Paid sick leave

Job summary

SpaceXAI is seeking an AI Humanities Specialist to annotate data and guide model behavior in humanities contexts. You will leverage linguistics, history, literature, philosophy, ethics, arts, and related fields to train Grok, ground outputs in primary sources, and distinguish evidence from interpretation.

You will collaborate with engineering teams to design evaluation tasks, develop high‑quality datasets, and craft precise, sourced answers that admit uncertainty when appropriate.

Qualifications

• Deep knowledge in at least one humanities field.
• Ability to assess arguments for validity, soundness, and premises.
• Habitual reliance on primary sources with weighing against secondary literature.
• Ability to steel-man opposing views and separate settled knowledge from interpretation.

Responsibilities

• Evaluate model outputs for factual accuracy, coherence, and hidden assumptions.
• Flag errors, anachronisms, misused sources, and ideological slant.
• Create exemplary responses and datasets with clear sourcing and distinctions.
• Steel-man opposing views before criticizing them and mark what is settled or unknown.
• Ground annotations in primary sources and weigh secondary scholarship accordingly.
• Collaborate with engineering to design evaluation tasks and rubrics.
• Help define good model behavior for contested humanistic questions.

Skills

Linguistics

History

Philosophy

Literature

Ethics

Classical studies

Visual arts

Performing arts

Education

PhD in humanities

Job description

SpaceXAI is seeking an AI Humanities Specialist to annotate data and guide model behavior in humanities contexts. You will leverage linguistics, history, literature, philosophy, ethics, arts, and related fields to train Grok, ground outputs in primary sources, and distinguish evidence from interpretation.

You will collaborate with engineering teams to design evaluation tasks, develop high‑quality datasets, and craft precise, sourced answers that admit uncertainty when appropriate.