Parth Suresh
About Parth Suresh
Parth Suresh is a Machine Learning Research Engineer at Scale AI, where he focuses on solving customer problems using Large Language Models. He has a background in data science and software engineering, with previous roles at Meta, Intel Corporation, and the Indian Institute of Science.
Work at ScaleAI
Parth Suresh currently serves as a Machine Learning Research Engineer at Scale AI, a position he has held since 2023. In this role, he focuses on addressing various customer challenges through the application of Large Language Models (LLMs). His work involves developing and implementing innovative solutions that leverage advanced machine learning techniques to enhance customer experiences.
Previous Experience at Meta
Parth Suresh has a notable history with Meta, where he worked in two distinct roles. He began as a Data Science Intern for three months in 2020, gaining foundational experience in data analysis and machine learning. Following this, he advanced to the position of Research Data Scientist from 2021 to 2023, where he contributed to various data-driven projects and initiatives.
Educational Background
Parth Suresh earned a Master of Science (MS) in Computer Science from the University of Southern California, where he studied from 2019 to 2021. Prior to this, he completed a Bachelor of Technology (BTech) in Computer Science and Engineering at TKM College of Engineering, Kollam, from 2015 to 2019. His academic background provides a strong foundation for his work in machine learning and data science.
Research Experience
Before joining Scale AI, Parth Suresh worked as a Graduate Student Worker (Research Assistant) at the Information Sciences Institute from 2019 to 2021. His research contributions during this time were significant in advancing knowledge in the field. Additionally, he gained experience as a Research Intern at the Indian Institute of Science (IISc) in Bangalore for two months in 2017, further enhancing his research skills.
Technical Skills and Specializations
Parth Suresh specializes in building models as a service, utilizing techniques such as finetuning, retrieval-augmented generation (RAG), and continued pretraining. His strong problem-solving and analytical skills enable him to quickly grasp new domains and apply first principles to develop effective solutions in machine learning.