Mechanical Design Engineer at

CONFLUX SYSTEMS

Mechanical Design Engineer at

CONFLUX SYSTEMS

in Bellevue, Washington Posted in 17 days ago.

Type:

full-time We are seeking a Mechanical Engineering Data Specialist to bridge traditional mechanical engineering expertise with modern AI-driven data operations . This role focuses on reviewing, curating, and annotating complex engineering datasets to support computational models, automated design systems, and data-centric engineering solutions. The ideal candidate will analyze CAD/CAE data , document design lifecycle changes, and develop structured guidelines that capture engineering intent, constraints, and design rationale.

Responsibilities:

Develop data protocols, annotation guidelines, and technical requirements for capturing engineering design intent and constraints for AI/ML training . Review and curate engineering datasets from CAD/PLM tools (NX, Teamcenter, SolidWorks, CATIA) and CAE platforms (Simcenter, ANSYS) to ensure accuracy and quality. Analyze design lifecycle changes, creating version comparisons and diff datasets to explain geometry and simulation modifications. Generate synthetic engineering datasets using CAD/CAE scripting , including NX Open and Python , to create part variations and simulation scenarios. Support structured engineering knowledge development using data annotation schemas, engineering ontologies, and knowledge representation frameworks .

Qualifications:

Bachelor's or Master's degree in Mechanical Engineering, Aerospace Engineering, or related field . 5+ years of hands-on experience with CAD/PLM tools , preferably Siemens NX and Teamcenter . Strong understanding of design-to-simulation workflows , including FEA, CFD, structural analysis, and engineering constraints . Proficiency in Python programming for automation, scripting, and engineering data manipulation. Experience with Data-Centric AI , non-text dataset preparation, 3D meshes, point clouds , or engineering field data. Knowledge of Cyber-Physical Systems (CPS) , safety assurance modeling, Graph Neural Networks (GNNs) , or geometric deep learning applied to engineering data is preferred.