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Invisible Technologies · 1 month ago

Human Resource Specialist - Freelance AI Trainer Project

United States of America

Full-time

Onsite

Mid, Senior Level

$6/hr - $65/hr

Agency is seeking human resource specialists to help shape the future of AI by providing expertise in various HR domains. The role involves interacting with advanced language models to improve their understanding of HR practices and ensuring the accuracy of their outputs.

Data Center AutomationInformation TechnologyProductivity ToolsProject Management

Responsibilities

Shape the future of AI with high-quality training data

Challenge advanced language models on topics like recruitment strategies, conflict resolution, workforce planning, HR analytics, employee engagement, benefits administration, and compliance auditing

Document every failure mode to harden model reasoning

Converse with the model on HR scenarios and theoretical workplace questions

Verify factual accuracy and logical soundness

Capture reproducible error traces

Suggest improvements to prompt engineering and evaluation metrics

Qualification

Talent acquisitionEmployee relationsPerformance managementLabor law complianceHR analyticsDiversityEquityInclusionTrainingDevelopmentWorkforce planningBenefits administrationCompliance auditingClear communicationConflict resolution

Required

Expertise in talent acquisition

Expertise in employee relations

Expertise in performance management

Expertise in compensation and benefits

Expertise in organizational development

Expertise in labor law compliance

Expertise in training and development

Expertise in diversity, equity, and inclusion (DEI)

Ability to challenge advanced language models on topics like recruitment strategies, conflict resolution, workforce planning, HR analytics, employee engagement, benefits administration, and compliance auditing

Ability to document every failure mode to harden model reasoning

Ability to converse with the model on HR scenarios and theoretical workplace questions

Ability to verify factual accuracy and logical soundness

Ability to capture reproducible error traces

Ability to suggest improvements to prompt engineering and evaluation metrics

Clear, metacognitive communication—'showing your work'—is essential