Governance, Risk & Compliance (GRC) Analyst (AI Training) We partner with the world's leading AI research teams and labs to build and train cutting-edge AI models. Right now, we're looking for experienced GRC practitioners to help us develop high-quality datasets and evaluation frameworks focused on security, risk, and compliance reasoning. This is a unique opportunity to apply your real-world knowledge of security policies, compliance programs, audits, and risk management to directly shape how AI understands and reasons about these critical domains. About The Role Organization: Alignerr Type: Hourly Contract Location: Remote Commitment: 10-40 hours/week What You'll Do Review and analyze security policies, controls, and procedures for accuracy and completeness Evaluate and classify compliance scenarios across frameworks such as SOC 2, ISO 27001, and NIST Assess risk statements, control mappings, and audit-style documentation Generate and validate training and evaluation data used to improve AI reasoning in GRC contexts Apply your professional judgment to identify gaps, ambiguities,



and errors in AI-generated compliance outputs Who You Are 2+ years of hands-on experience in GRC, compliance, risk management, or a related field Solid familiarity with one or more frameworks - SOC 2, ISO 27001, NIST, CIS, or similar Comfortable reading, interpreting, and critiquing policy documents, audit reports, and control frameworks Detail-oriented with strong written reasoning and communication skills Self-motivated and able to work independently on asynchronous, task-based assignments Nice to Have Experience with data annotation, data quality review, or AI evaluation workflows Background in information security, IT audit, or enterprise risk Familiarity with privacy regulations such as GDPR, HIPAA, or CCPA Why Join Us Work directly on frontier AI systems alongside top research labs Fully remote and versátil - work on your own schedule Freelance perks: autonomy, variety, and global c

📌 Governance, Risk (Madrid)
🏢 Alignerr
📍 Madrid