Why This Role Stands Out
This remote Mechanical Design Engineer role offers a unique opportunity to blend traditional engineering skills with cutting-edge AI-driven data operations, perfect for those eager to shape the future of design. You'll thrive by leveraging your expertise in CAD/CAE and developing innovative data protocols, contributing to impactful AI/ML training in a flexible, project-based environment.
Quick Overview
Job Description
100% Remote, 6 Months Contract
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.
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