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Data Annotation Tech
Mindrift
TolokaNo subject matter listed
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TextOver the past few years, I’ve worked on several AI training data and model evaluation projects focused on improving large language model performance and alignment. My role has involved supporting supervised fine-tuning workflows through response annotation, output ranking, and detailed feedback grounded in structured evaluation rubrics. I’ve reviewed model outputs for factual accuracy, reasoning quality, instruction-following, tone, and policy compliance, and I’ve participated in RLHF-style evaluation processes where responses are compared, graded, and analyzed for improvement. Beyond standard annotation, I’m particularly interested in probing how models behave under ambiguity or edge-case prompts. I’ve tested outputs for hallucinations, bias, subtle reasoning gaps, and contextual misinterpretation, documenting patterns rather than isolated errors. Through work with organizations such as T-Maxx International, Mindrift, RWS, and DataAnnotation, I’ve contributed to quality assurance pipelines across tasks including translation review, fact-checking, response grading, and dataset refinement. I bring a research-oriented mindset and strong attention to nuance, which allows me to identify deeper alignment and robustness issues that may not be immediately obvious.
Analyzed user questions and corresponding AI responses, and labeling outputs. Ensured responses met accuracy, clarity, and relevance standards.
Scholarship Certificate, Data Science
Certificate, Product Management and Product Marketing
Independent Consultant
Founder
