Alignerr Data Labeling
I have worked on various projects related to LLM code quality evaluation, mathematics, code human preference, and coding, math, and physics applications.
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I am a software engineer and data scientist with hands-on experience in building large-scale data pipelines, annotation workflows, and machine learning systems across domains like NLP, finance, and code quality assessment. My expertise includes processing and validating massive datasets (100GB+, 100M+ rows), designing ETL pipelines, and developing automated data enrichment and labeling solutions using Python, Pandas, Dask, and cloud platforms such as AWS and Azure. I have worked extensively with NLP models (BERT, GPT, T5), fine-tuned transformers for domain-specific tasks, and engineered end-to-end data workflows for tasks like sentiment analysis, classification, and feature extraction. My background in statistical analysis, experiment tracking, and MLOps ensures high data quality and reproducibility for AI training data projects. I am passionate about building robust, scalable systems that power accurate and reliable machine learning applications.
I have worked on various projects related to LLM code quality evaluation, mathematics, code human preference, and coding, math, and physics applications.
Bachelor of Science, Communication and Information Engineering
Junior Data Scientist
Full Stack Software Engineer