Summary
Hemit Shah is a software engineer and Machine Learning MS student at Carnegie Mellon with eight years of experience building data-driven systems and deploying ML models across companies like Snowflake, Wish, CIBC, and Natural Resources Canada. He combines practical software engineering—shipping C-accelerated Python modules and async SQL execution features—with applied ML work such as graph-based product embeddings, NLP clustering, and satellite-imagery pipelines. His internships spanned the full ML lifecycle from feature engineering and scalable Airflow/Spark pipelines to production connector improvements, reflecting a comfort with both researchy prototyping and production-grade performance optimizations. Based in Bellevue, WA, he’s particularly interested in the systems and distributed engineering challenges behind training and serving foundation models, and has repeatedly reduced latency and compute through algorithmic and engineering trade-offs.
8 years of coding experience
2 years of employment as a software developer
Master of Science - MS, Machine Learning, Master of Science - MS, Machine Learning at Carnegie Mellon University
Bachelor of Software Engineering (BSE), Software Engineering, Bachelor of Software Engineering (BSE), Software Engineering at University of Waterloo
English, Gujarati