What the work actually involves

You are producing reference-grade engineering artifacts and the reasoning behind them. A typical task might ask you to model a part from a written requirement set, fully dimension a 2D drawing with correct GD&T callouts, and then write out why you chose that datum scheme, that tolerance stack, that process. Other tasks run the opposite direction: you are handed a model or drawing — sometimes AI-generated, sometimes deliberately flawed — and asked to find what breaks in manufacturing, rank the errors by severity, and rewrite the documentation so a machinist could actually work from it.

Expect a mix of SolidWorks, Fusion 360, Onshape, Inventor, Creo and NX across assignments, with FreeCAD appearing specifically because it is open-source and models in it can be inspected programmatically. You do not need to be an expert in FreeCAD, but you should be able to open, navigate and interpret a FreeCAD model without being slowed to a halt. DFM commentary — draft angles, wall thickness, tool access, fixturing, material substitution — is a recurring theme, because that is exactly the kind of tacit shop-floor knowledge current models get wrong.

What the screening looks for

  • Parts that shipped. Reviewers want specific components you took from concept through to production, with the process named (3-axis milling, injection moulding, sheet metal, investment casting) and the problems you hit.
  • GD&T under follow-up. Expect to be pushed past terminology into datum selection, material condition modifiers, and when a position tolerance beats a coordinate dimension.
  • Written clarity. A large fraction of the deliverable is prose. Vague, hedging explanations are the most common reason strong engineers score poorly here.
  • Honest scope. Claiming expert fluency in six CAD packages invites a follow-up that will find the floor quickly.

Logistics

Contractor engagement, fully remote, asynchronous. Most contributors work in blocks of 10–20 hours a week against a task queue, with some flexibility on when those hours land; commitment expectations vary by project and are set before you start. No prior AI or machine-learning experience is required — the whole premise is that your engineering judgment is the scarce input. Pay in the $40–80/hr range has been observed on this listing; the figure is not guaranteed and generally tracks seniority and the breadth of tooling you can genuinely cover.