Remote Mechanical Engineer AI Trainer

Overview We are looking for experienced Mechanical Engineers to evaluate how frontier AI models handle real engineering work: sizing a component against a real load case, reading a CAD assembly and spotting what interferes at full travel, a tolerance stack up, a thermal or fluids calculation, a DFM decision, a failure analysis. The models can already talk fluently about mechanical engineering; what they cannot yet do reliably is the actual work. You bring the judgment you have built catching the failure mode the spec sheet does not mention. We bring the model output that judgment is needed to grade.

• In this role, you will design challenging, realistic tasks drawn from your own practice, such as a component sizing calculation with a worked solution, a tolerance stack up, a CAD assembly review with interference and clearance findings, a thermal or fluids analysis, a DFM and DFA review, a failure analysis report, or a test and validation plan, run them through frontier AI agents, and evaluate what comes back against a professional standard.
• You will work with realistic professional files, the kind a practitioner in your field actually handles, which you assemble yourself. Some tasks are compact, built around a handful of files; others are larger scenarios that take several days to build. In every case the goal is the same: a task a competent professional in your field would complete correctly and a frontier model currently gets wrong.
• This is not a traditional mechanical engineering role. You will be helping build better AI by putting your knowledge to work in a structured, flexible, fully remote environment.
• The work is long form and self directed, and clear written reasoning matters as much as technical depth.

Description

• Design challenging, realistic mechanical engineering tasks drawn from your own day to day work: the scenario, a prompt phrased the way you would brief a trusted colleague, and the supporting files an engineer would need (drawings, CAD models, load cases, material data, test data, specifications), which you author yourself.
• Run those tasks through frontier AI models and evaluate the deliverable they produce (the calculation, design review, analysis or report) against the standard you would hold a colleague to.
• Compare two model outputs on identical prompts and files, decide which performed better, and document where each fell short.
• Write closed ended problems with worked solutions and acceptance criteria, and detailed grading rubrics that specify what a correct deliverable must contain, such as the right loads and factors, the right material, the right tolerances and the right failure modes considered, and explain in writing why a response passes or fails each one.
• Flag concrete failures with evidence: wrong boundary conditions, unit errors, interferences missed, tolerance stacks that do not close, unsafe factors of safety, fabricated or ignored source files, and off brief interpretation of the ask.
• Contribute across design, analysis, manufacturing and test, and review and refine tasks built by other engineers.

Qualifications

• US Citizenship
• 2 years of relevant experience.
• In progress Bachelors degree or higher in mechanical engineering or a related field.
• Depth in at least one of: mechanical design and CAD (geometry, clearances, tolerances, mechanisms); structures and materials (stress, deflection, fatigue, buckling, material selection, failure analysis); thermal and fluids (heat transfer, thermodynamics, HVAC, pumps and piping); dynamics and controls (vibration, kinematics, rotating machinery); manufacturing (DFM and DFA, process selection, GD&T, tolerance stack ups, cost and quality trade offs).
• Fluency in the tools of your specialty: CAD (SolidWorks, Fusion 360, Onshape, Creo, NX, CATIA or Inventor), FEA or CFD, or MATLAB or Python for engineering analysis.
• General familiarity with AI and LLM tools. You have used models like Claude or ChatGPT in professional work and have the judgment to tell a well reasoned answer from a plausible sounding but incorrect one.
• Baseline tech literacy: comfortable with cloud file tools (e.g., Google Workspace), managing browser profiles, downloading and installing desktop apps (e.g., Claude), and everyday file handling (e.g., converting between Excel and Google Sheets, zipping files for sharing).

Additional Information

• 20 openings available.
• No interview required. A technical assessment takes the p…