Sarita Hedaya
About Sarita Hedaya
Sarita Hedaya is a Machine Learning Engineer at Latent AI, where she plays a key role in product planning and the development of a modular ML training platform. She has previous experience as an intern at various organizations, including the New York Stem Cell Foundation Research Institute and Stevens Institute of Technology.
Work at Latent AI
Sarita Hedaya has been employed as a Machine Learning Engineer at Latent AI, Inc. since 2021. In this role, she plays a key part in product planning and road mapping. She leads the development of a modular machine learning training platform known as the Application Framework. This platform is designed to optimize inference times, energy usage, and memory consumption. Additionally, she collaborates with customer-facing team members to identify and implement new features and fixes for the Application Framework.
Education and Expertise
Sarita Hedaya studied at Stevens Institute of Technology, where she earned a Bachelor's degree in Software Engineering from 2017 to 2020. She furthered her education at the same institution, obtaining a Master's degree in Machine Learning from 2019 to 2020. Her academic background provides her with a strong foundation in software development and machine learning principles.
Previous Work Experience
Before her current position, Sarita Hedaya gained valuable experience through various internships. She worked as an Undergraduate Research Assistant at Stevens Institute of Technology for four months in 2019. She also served as a Machine Learning Intern at The New York Stem Cell Foundation Research Institute for two months in 2019. Earlier in her career, she interned at Fémur Arquitectura in Panama for two months in 2013 and at Credicorp Bank, S.A. for one month from 2012 to 2013.
Key Projects and Contributions
Sarita Hedaya leads the development of the Application Framework, a modular machine learning training platform. This project focuses on optimizing critical aspects such as inference times, energy consumption, and memory usage. Her work on this platform reflects her commitment to advancing machine learning technologies and improving their efficiency.