OpenSees Structural Engineering Expert for AI Training Data Review

We are developing a next-generation AI tool for structural engineering and are seeking an experienced OpenSees developer to review and validate its training data and modeling examples. The main goal is to improve the accuracy, reliability, and technical quality of the AI-generated OpenSees content. You will evaluate structural models, scripts, assumptions, analysis procedures, and expected outputs to identify errors or unrealistic results. **Responsibilities** – Review OpenSees training examples, scripts, and structural modeling workflows – Verify material properties, elements, boundary conditions, loads, constraints, and analysis settings – Identify incorrect, incomplete, or misleading modeling examples – Correct technical errors and recommend better modeling approaches – Assess whether AI-generated outputs follow accepted structural engineering practices – Provide clear explanations and improved examples for AI training **Required Experience** – Strong practical experience with OpenSees and structural engineering modeling – Proficiency in Tcl and/or OpenSeesPy – Experience with nonlinear static, dynamic, or seismic analysis – Strong understanding of finite element modeling and structural behavior – Ability to review technical content carefully and explain corrections clearly – Experience creating or validating engineering datasets is preferred **Deliverables** – Reviewed and corrected OpenSees training examples – Documentation of identified errors and recommended improvements – Technically accurate replacement examples where necessary – General recommendations for improving the AI tool’s structural modeling accuracy Please include examples of OpenSees projects you have completed and briefly describe your experience reviewing or validating structural engineering models.