Mechanical Design And Analysis AI Training Expert

OpenTrain AI, Inc.; United States; À distance; USD 55 000 - 83 000; Plein temps

Résumé du poste

OpenTrain AI, Inc. is seeking contributors to create and evaluate datasets from real mechanical engineering workflows. You will transform complex design challenges into structured, verifiable problems with clear physical ground-truth criteria.

The role involves designing scoring rubrics, validating stress/convergence criteria, and coordinating distributed engineers. Strong English communication and a background in CAD/CAE are essential for success in this remote contract engagement.

Qualifications

• Bachelor's or Master's in a mechanical/ aerospace/ automotive field or related discipline.
• Solid grounding in 3D CAD and GD&T for real-world problems.
• Experience with FEA/CFD, prototype testing, and design for manufacturability.
• Familiarity with major CAE tools and simulation platforms used in industry.

Responsabilités

• Convert mechanical workflows into structured, verifiable problems with clear ground-truth criteria.
• Create scoring rubrics and validation rules for geometry, tolerances, and meshing.
• Evaluate AI training datasets and diagnose failure modes in engineering tasks.
• Coordinate distributed engineers and technical contributors across time zones.
• Design automated quality checks to protect dataset integrity.

Connaissances

3D parametric CAD

DFM/DFA

GD&T

FEA/CFD

Prototype testing

FMEA

English proficiency

Formation

Bachelor's or Master's in mechanical/aerospace/automotive engineering or related field

Outils

SolidWorks

CATIA

Siemens NX

ANSYS

Altair HyperWorks

Abaqus

Description du poste

The work

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.

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.