Summary
Mohammad Dehghan is a Senior Machine Learning Engineer with a decade of experience specializing in natural language processing, currently building NLP systems at Autodesk after research roles at Huawei Noah’s Ark Lab and the University of Waterloo. He has led production-grade Retrieval-Augmented Generation and document QA pipelines, co-authored an ACL 2024 paper, and improved QA performance by 13% through adaptive pretraining and knowledge distillation. Comfortable spanning research and engineering, he has hands-on experience fine-tuning LLMs with DeepSpeed, deploying models on AWS SageMaker, and integrating retrievers, rerankers, and web scrapers into robust ML pipelines. His academic work on unsupervised text simplification (Findings of ACL 2022) and early focus on machine translation reflect a long-standing interest in making language models both accurate and accessible across languages and domains.
9 years of coding experience
4 years of employment as a software developer
high-school diploma, student, high-school diploma, student at National Organization for Development of Exceptional Talents (Sampad)
Bachelor of Engineering - BE, Computer Software Engineering, 18.3/20 (Last two years), Bachelor of Engineering - BE, Computer Software Engineering, 18.3/20 (Last two years) at Isfahan University of Technology
Master of Mathematics, Computer Science, 97/100, Master of Mathematics, Computer Science, 97/100 at University of Waterloo