B.Sc. or M.Sc. in Mechanical Engineering, Biomedical Engineering, or a related field field Expertise in biomechanics, human factors, and/or anthropometrics, with the ability to connect biological and physiological variability to a physical device performance Strong Python proficiency for scientific computing, simulation, and tooling development Proven experience with data analysis and visualization, with a sharp ability to present insights in a clear and actionable format Hands-on experience designing and building end-to-end experimental hardware setups Familiarity with embedded electronics platforms (Arduino, Raspberry Pi, ESP32, etc.) and basic EE integration Experience with at least several of the following sensing modalities: IMUs, strain gauges, 3D scanning, photogrammetry, Digital Image Correlation (DIC), EEG Excellent verbal and written communication skills, with the ability to convey technical concepts to non-specialist audiences Highly self-directed, with a demonstrated capacity for independent learning in new technical domains 5+ years of industry or research experience in a relevant field Experience in complex, multi-disciplinary device development with human-factors interface research aspects Background in statistical modeling of population distributions or human physiological variability Proficiency in mechanical CAD tools (NX preferred) for fixture and experimental setup design Experience defining hardware or EE requirements in a cross-functional product development context Background in signal processing, algorithm development, or control systems Experience working within an advanced research or research-to-product pipeline
