You will create and evaluate datasets based on real mechanical engineering workflows. The work turns complex design and analysis challenges into structured problems that AI systems can solve against clear physical engineering criteria.
- Convert engineering challenges into test scenarios with defined inputs, requirements, geometric constraints, kinematic constraints, and verified reference solutions.
- Create scoring rubrics and validation rules for stress concentrations, mesh convergence, boundary conditions, tolerance stacks, and GD&T adherence.
- Evaluate AI agents, diagnose failure modes, and define the physical criteria used to judge their work.
- Design automated and expert-reviewed quality checks that protect dataset quality.
- Coordinate distributed engineers and technical contributors as training and evaluation datasets are developed.
What it pays and takes
This is a remote contract engagement planned for eight weeks, with potential extension opportunities. Pay is not specified in the listing.
- Work arrangement: Remote contract engagement.
- Planned length: Eight weeks, with potential extension opportunities.
- Time commitment: 40 hours per week.
- Experience level: Entry level.
- Education: Bachelor's or master's degree in mechanical engineering, aerospace engineering, automotive engineering, or a closely related physical discipline.
- Industry experience: 15 or more years delivering hardware from concept through production in automotive, aerospace, or advanced precision engineering.
- Technical skills: 3D parametric CAD, DFM/DFA, GD&T, FEA or CFD simulation, prototype testing, and FMEA.
- Software experience: SolidWorks, CATIA, Siemens NX, ANSYS, Altair HyperWorks, or Abaqus.
- Problem design: Ability to turn open-ended mechanical workflows into structured, verifiable problems with clear physical ground-truth criteria.
- Coordination: Experience coordinating distributed engineers or technical contributors.
- Language: Strong written and verbal English.
How it works
You apply on OpenTrain with your resume and then complete the application on the hiring site.
About AI training work
AI training work uses human-created examples, evaluations, and feedback to improve how artificial intelligence systems perform. Experienced engineers help by setting reliable standards for specialized tasks and checking whether model outputs meet real-world requirements.