Jorge L. Valderrama
About Jorge L. Valderrama
Jorge L. Valderrama is a GNC Engineer I at Rocket Lab in Auckland, New Zealand, specializing in guidance, navigation, and control for launch vehicles. He has a background in mechanical and aerospace engineering, with experience in finite element analysis and multidisciplinary design optimization.
Work at Rocket Lab
Jorge L. Valderrama currently serves as a GNC Engineer I for the Neutron project at Rocket Lab, a position he has held since 2023. His role involves working on guidance, navigation, and control systems essential for the launch vehicle's performance. He is based in Auckland, New Zealand, contributing to the development of advanced aerospace technologies.
Previous Experience in Aerospace Engineering
Before joining Rocket Lab, Valderrama worked at Astos Solutions as a Launch Vehicle Guidance, Navigation, and Control Engineer from 2022 to 2023 in Stuttgart, Germany. His previous roles include an internship at ONERA - The French Aerospace Lab, where he focused on launch vehicle uncertainty quantification in 2021, and a collaborative research project at ISAE-SUPAERO on launch vehicle optimization from 2020 to 2021.
Education and Expertise
Valderrama holds a Master of Science in Aerospace, Aeronautical and Astronautical Engineering from ISAE-SUPAERO, completed between 2019 and 2021. He also earned a Bachelor of Science in Mechanical Engineering from Universidad EAFIT, where he studied from 2013 to 2019. His education includes an exchange semester at Queensland University of Technology in 2015. He possesses expertise in finite element analysis, multibody simulation, and control of launch vehicles.
Background in Engineering
Jorge L. Valderrama has a diverse background in engineering, having worked as a Drivetrain Engineer for a solar car racing team at Universidad EAFIT from 2014 to 2016 in Medellín, Colombia. He also gained experience as a Computer Aided Design engineering intern at IECOS in Monterrey, Mexico, from 2018 to 2019. His work has focused on multidisciplinary design optimization and uncertainty quantification.