AI Quality Analyst, Personalization (Portuguese)

You will evaluate how conversational AI uses personal context to make responses more relevant and helpful. The work combines prompt design, response review, side-by-side ranking, and clear written feedback.

• Create realistic multi-turn prompts, usually spanning one to five turns, based on personal experiences.
• Check whether responses follow the starting prompt and use personalization appropriately.
• Review claims about the user for evidence, accuracy, and unsupported inferences.
• Assess whether personal information is used naturally instead of being unnecessarily repeated or narrated.
• Compare responses and rank them for helpfulness, usability, enjoyment, and overall quality.
• Write concise rationales that reference specific conversation turns and explain strengths and weaknesses.

This is a part-time contractor role for an intermediate-level contributor. You should be comfortable making careful judgments about nuanced or ambiguous AI responses and working independently.

• Pay: $15 per hour.
• Time: 20 or more hours per week.
• Location: Open worldwide; the role is remote.
• Language: Strong Portuguese reading and writing ability.
• Requirements: Excellent analytical judgment, creative multi-turn prompt writing, attention to detail, reliable computer and internet access, and effective remote communication.
• You should understand personalization quality, including grounding, incorrect personalization, poor inferences, forced connections, and naturalness.
• Clear, structured written communication is required.
• Helpful background: A bachelor's degree or equivalent experience in policy, law, ethics, linguistics, journalism, computer science, or a related analytical field.
• Preferred experience: Data annotation, AI quality evaluation, content moderation, or related work.

OpenTrain is a platform where people find AI training and data-labeling work. Apply on OpenTrain with your resume, then complete the application on the hiring site.

AI training is the human work behind many AI systems. People write prompts, review model responses, rank outputs, and give structured feedback so models can become more accurate, useful, and natural.