Maya Gilad

Maya Gilad has a diverse work experience in the field of engineering and software development. Maya is currently working at Voyantis as an ML Engineering Team Leader and Engineering Guild Master. Prior to this, Maya worked at Gett as an Algo Architect, where they mentored and guided data engineers and data scientists in the Algo and Data group. Maya also collaborated with team leaders to improve the time-to-market of research projects and transformed the group's MLOps vision into a reality. Earlier in their career, Maya worked as an Algorithm Developer at Gett, designing and implementing the group's research infrastructure and developing AI solutions to optimize Gett's marketplace.

Before joining Gett, Maya worked at Simplex as a Software Engineer, where they designed and implemented event streaming pipelines and developed microservices for real-time fraud decisions. Prior to that, they worked at MOD in various roles, including R&D Team Leader, Tech Lead, Development Team Leader, and Software Developer. In these roles, Maya managed teams of software engineers, led recruitment and training processes, and designed complex technical solutions for visual and geographical research questions. Maya also collaborated with computer vision researchers and implemented automated distributed computing engines. Overall, Maya has a strong background in engineering leadership and software development, with expertise in machine learning, data analysis, and algorithm development.

Maya Gilad's education history begins in 2008 when they completed software development training at the IDF Academy of Computers and Cyber Professions. From 2013 to 2017, Maya earned a Bachelor of Science (B.Sc.) in Computer Science from the Open University of Israel. Following this, they pursued a Master of Science (M.Sc.) in Computer Science at Reichman University from 2018 to 2023. Maya's educational journey culminated in their current pursuit of a Doctor of Philosophy (PhD) in Biomedical Engineering at the Technion - Israel Institute of Technology, which started in 2023 and is expected to conclude in 2026.