Language: Turkish

As an AI Quality Analyst, you will evaluate a new personalization feature for Gemini. You will assess how well the model uses information from your past Gemini conversations, Gmail, Google Search, and YouTube activity to make responses more relevant and helpful. This role requires a unique blend of creativity and analytical rigor. You will actively design prompts from the perspective of your own personal experiences. This work may involve recording or sharing personal data (for example voice, video or account activity): read the consent and privacy terms on the official page before applying.

We are looking for candidates with strong analytical and English comprehension skills. The ideal candidate should have the ability to read, summarize, and break down large content into smaller logical blocks, conduct research online, validate claims made in the content through online research, and work with the LLM (Large Language Models) to solve puzzles! Your role is critical in helping fine-tune and improve large language models (like gpt), and will make you an expert on how to leverage AI to be a better… Main tasks listed: You would spend time answering a variety of interesting analytical questions and creating scenarios that can train the LLM models to get better. Here are a couple of examples which models might get wrong and you would have to give the correct answer and explanation to enable the models to learn:; Based on a given distribution of sales by month across locations, could you analyze which location has grown the most? (Hint: what time period should we look at? should we account for sudden variability at the beginning?); In a small town, there are four distinct neighborhoods: Oak, Pine, Maple, and Elm. A postman is assigned to deliver mail and can only deliver to two neighborhoods in one day, with certain rules (e.g. Oak is always visited before Pine). If he delivers to Oak and Elm on the first day, which neighborhoods does the carrier deliver to on the second day?; Note: No other prior specialized domain experience is needed.

We are looking for candidates with strong analytical and English comprehension skills. The ideal candidate should have the ability to read, summarize, and break down large content into smaller logical blocks, conduct research online, validate claims made in the content through online research, and work with the LLM (Large Language Models) to solve puzzles! Your role is critical in helping fine-tune and improve large language models (like gpt), and will make you an expert on how to leverage AI to be a better… Main tasks listed: You would spend time answering a variety of interesting analytical questions and creating scenarios that can train the LLM models to get better. Here are a couple of examples which models might get wrong and you would have to give the correct answer and explanation to enable the models to learn:; Based on a given distribution of sales by month across locations, could you analyze which location has grown the most? (Hint: what time period should we look at? should we account for sudden variability at the beginning?); In a small town, there are four distinct neighborhoods: Oak, Pine, Maple, and Elm. A postman is assigned to deliver mail and can only deliver to two neighborhoods in one day, with certain rules (e.g. Oak is always visited before Pine). If he delivers to Oak and Elm on the first day, which neighborhoods does the carrier deliver to on the second day?; Note: No other prior specialized domain experience is needed.

We’re looking for Turkish language specialists who live and breathe phonology, morphology, syntax, semantics, translation, sociolinguistics, and Turkish linguistic history. You’ll challenge advanced language models on topics like agglutinative verb structure, vowel harmony, suffixation rules, compound word formation, regional dialect variations, idiomatic usage, and contextual nuances—documenting every failure mode so we can harden model reasoning. On a typical day, you will converse with the model on language scenarios, verify factual accuracy and logical soundness, capture reproducible error traces, and suggest improvements to our prompt engineering and evaluation metrics.