About the role
Validate relationships, classifications, and recommendations produced by automated systems
Compare outputs from different models and provide structured qualitative feedback
Identify and log errors, inconsistencies, or hallucinations in data or AI responses
Collaborate with data science and product teams to refine validation workflows
Use internal tools or dashboards to track validations, feedback, and patterns over time
Contribute to the development of SOPs, guidelines, and quality benchmarks
🧠 What We’re Looking For1–5 years of experience in data QA, content validation, annotation, or ML evaluation
Strong attention to detail and ability to identify semantic or factual inaccuracies
Clear written communication for documentation and feedback reporting
Comfortable working with structured and unstructured data
Familiarity with working alongside data science, product, or engineering teams
Bonus: Experience in domains like recruitment, HRTech, skills data, or classification systems
💡 Nice to HaveExposure to LLMs (e.g., ChatGPT, Claude) or evaluation of generative AI outputs
Prior experience with content curation, annotation tools, or ontology/tagging projects
Comfort with basic tools like Excel, Google Sheets, Airtable, or QA dashboards
✅ Why Join UsWork on cutting-edge AI systems with direct business impact
Be part of a fast-growing team that blends human insight with ML automation
Gain exposure to real-world applications of data quality and AI evaluation
Opportunities for growth into QA leadership, prompt validation, or ML feedback ops