Dariem Pérez Herrera
About Dariem Pérez Herrera
Dariem Pérez Herrera is a Senior Software Engineer specializing in Machine Learning Platforms at BenchSci in Toronto, Canada. He has extensive experience in software development and engineering, having worked in various roles across multiple companies since 2006.
Current Role at BenchSci
Dariem Pérez Herrera currently serves as a Senior Software Engineer for the Machine Learning Platform at BenchSci. He has been in this role since 2024 and is based in Toronto, Ontario, Canada. His responsibilities include providing support and improvements to a self-served machine learning platform that facilitates the productionization of self-hosted large language models (LLMs).
Previous Experience at BenchSci
Prior to his current position, Dariem held multiple roles at BenchSci. He worked as a Data Engineer for Science Tools from 2020 to 2021 and then transitioned to Senior Data Engineer for Science Tools from 2021 to 2022. Dariem also served as a Senior Software Engineer 1 for Machine Learning Operations (MLOps) from 2022 to 2024, where he integrated optimized LLM serving solutions into the ML platform.
Professional Background
Dariem has extensive experience in software development and engineering. He worked as a Full Stack Developer at SRP Systems and Device42, both as an independent contractor. He has also served as a Software Engineer at Dessa and as a Partner and Technical Advisor at Inforsoldes, S.A.S. His earlier roles include positions as a Lecturer and Lead Software Developer at Universidad de las Ciencias Informáticas in Havana, Cuba.
Education and Qualifications
Dariem earned his Bachelor's degree in Computer Science from Universidad Central 'Marta Abreu' de Las Villas, where he studied from 2001 to 2006. He furthered his education by obtaining a Master of Science (MSc) in Computer Science from the same university between 2012 and 2013. His academic background supports his extensive career in software engineering and machine learning.
Technical Contributions
Dariem has made significant contributions to machine learning technologies. He has benchmarked vector databases specifically for Retrieval-Augmented Generation (RAG) applications and conducted assessments of commercial and open-source LLMs to evaluate cost-effectiveness and throughput. His work has focused on enhancing the capabilities of machine learning platforms.