About the Company:
Our client is a Palo Altobased AI infrastructure and talent platform founded in 2018. It helps companies connect with remote software developers using AI-powered vetting and matching technology. Originally branded as the Intelligent Talent Cloud, it enabled companies to spin up their engineering dream team in the cloud by sourcing and managing vetted global talent.
In recent years, they have evolved to support AI infrastructure and AGI workflows, offering services in model training, fine-tuning, and deploymentpowered by their internal AI platform, ALAN, and backed by a vast talent network. They reported $300 million in revenue and reached profitability. Their growth is driven by demand for annotated training data from AI labs, including major clients like OpenAI, Google, Anthropic, and Meta.
Job Description:
Job Title: AI Quality Analyst (Data annotation&content moderation) -Japanese/Chinese/Korean/Tai Languages
Location: Pan India
Experience: 3+ yrs
Employment Type: Contract to hire
Work Mode: Remote
Notice Period: - Immediate joiners
Key Responsibilities
- In this role, you will be part of a dynamic team focused on evaluating the quality of personalised AI interactions. Your day-to-day work will involve:
- Designing and executing multi-turn conversational prompts (typically 1-5 turns) that require the AI to utilise your personal information and experiences.
- Evaluating model responses based on your intent from the starting prompt, checking if the personalisation was appropriately applied.
- AI Quality Analyst (Data annotation&content moderation) -Japanese/Chinese/Korean/Tai Languages
- Analysing responses for grounding issues, ensuring claims about you are supported by evidence and not flawed inferences or hallucinations.
- Assessing integration quality to ensure personal data is woven naturally into the response without robotic overnarrating.
- Rigorously evaluating and stack-ranking two model responses side-by-side (SxS) to determine which is overall more helpful, easy to use, and enjoyable.
- Writing clear, defensible rationales for your comparisons, explicitly referencing where issues or positive aspects occurred in the conversation.
- Extracting and verifying Debug Info from the model to confirm that chat summaries and data sources were properly utilised.
- Maintaining strict data hygiene by deleting evaluation conversations to prevent them from polluting your future chat history.
