SWE-Bench Pro dataset
SWE-Bench Pro is a large-scale benchmark dataset for software engineering tasks where AI systems must understand real GitHub issues, analyze the underlying codebase, implement a correct fix, and pass the project’s test suite
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I have strong experience working with AI training data, particularly in data labeling, model evaluation, and quality assurance. At Outlier, I contributed to several large-scale AI projects and was part of the team involved in the creation of the SWE-Bench Pro dataset, which focuses on real-world software engineering problem solving. This work required a high level of precision, consistency, and analytical reasoning, as well as the ability to follow complex technical guidelines. My background in mathematics and programming allows me to approach labeling and evaluation tasks with rigor and attention to edge cases. I am comfortable working with structured and unstructured data, assessing model behavior, and ensuring data quality under tight deadlines. I am highly motivated to continue contributing to AI development through reliable and high-impact training data.
SWE-Bench Pro is a large-scale benchmark dataset for software engineering tasks where AI systems must understand real GitHub issues, analyze the underlying codebase, implement a correct fix, and pass the project’s test suite
Worked on an AI training project focused on solving mathematics problems presented in image format, including handwritten and printed material. Tasks involved interpreting the visual content, understanding the mathematical structure, and producing accurate step-by-step solutions.
Participated in an AI training project focused on annotating quail behavior in video footage. Each clip was labeled according to predefined categories such as bathing, eating, or sleeping, ensuring consistency, accuracy, and adherence to detailed behavioral guidelines.
Diploma, Data Science
Bachelor's Degree, Mathematics
Software Developer
Assistant Professor