LLM Safety Evaluator - Cantonese

We are hiring an LLM Safety Evaluator - Cantonese to support AI safety, adversarial evaluation, and language model quality testing for a leading technology client.

This role is focused on LLM red teaming and adversarial safety evaluation. The ideal candidate will have hands-on experience evaluating language model outputs, deliberately testing models to identify unsafe or harmful responses, and translating those findings into preventive frameworks, evaluation criteria, and actionable insights.

This is not a BI/dashboard-heavy role. Tableau and reporting experience are helpful, but the main priority is experience with LLM output evaluation, AI safety, red teaming, localization, linguistics, and Cantonese language expertise.

Key Responsibilities:

• Evaluate the quality and safety of LLM/generative AI outputs.
• Perform adversarial testing and red teaming to identify harmful, unsafe, biased, or policy-violating model responses.
• Create and improve adversarial datasets, stress-test datasets, and model evaluation criteria.
• Analyze model behavior across Cantonese language, culture, and market-specific contexts.
• Translate qualitative findings into clear, actionable insights for model improvement.
• Support evaluation frameworks that measure model safety, policy alignment, and response quality.
• Use SQL and Python for data analysis where required, including pandas, NumPy, and Jupyter.
• Collaborate with cross-functional teams working on AI safety, localization, linguistics, and model evaluation.

Required Qualifications:

• Native Cantonese proficiency is mandatory.
• Hands-on experience evaluating LLM or generative AI outputs for quality, safety, harmful content, bias, or policy violations.
• Experience with LLM red teaming, adversarial prompt testing, AI safety evaluation, or model behavior analysis.
• Background in localization, linguistics, trust and safety, content quality, language operations, or AI safety.
• Ability to build or contribute to adversarial/stress-test datasets and evaluation frameworks.
• Ability to translate qualitative and quantitative findings into actionable insights.
• Working knowledge of SQL and/or Python for data analysis is preferred.

Preferred Qualifications:

• Experience with Python libraries such as pandas, NumPy, or Jupyter.
• Experience working with large-scale AI evaluation datasets.
• Experience with safety policy evaluation, content moderation, annotation, linguistic QA, or localization QA.
• Familiarity with Tableau or reporting dashboards is a plus, but not required.
• Bachelor’s degree in Data Science, Linguistics, Computer Science, Data Analytics, or a related field is preferred, but not a dealbreaker.