FreeCAD / Mechanical CAD Expert, AI Computer-Use Trainer

## About SME Careers

As AI systems become more sophisticated, they rely on human knowledge to better understand context and the world. At SME Careers, we partner with leading AI labs to improve how the next generation of intelligent systems learn, reason, and act. SME Careers is a fast-growing AI data services company and subsidiary of SuperAnnotate that provides AI training data to many of the world's top AI companies and foundation model labs. Your expertise in parametric CAD will directly help improve the world's premier AI models.

## About the Role

AI agents are getting good at reading a screen and clicking through web forms. Put one in front of a parametric CAD package and it falls apart. FreeCAD is an unforgiving test case: multiple workbenches with overlapping capabilities, a constraint solver that fails in ways the interface doesn't explain, feature histories where the correct next action depends entirely on what was built twenty steps earlier, and the topological naming problem lying in wait for anyone who references geometry carelessly. Engineering intent lives in the model tree, not on the screen — and that is exactly what current agents cannot see.

In this remote, hourly contract role you will help close that gap. You will design realistic, challenging CAD tasks, then record yourself completing them on your own computer using recording tools we provide. Each recording captures the full picture of how expert work actually gets done, the sequence of actions, the interface state at every step, and the files before and after, and becomes both a demonstration the model learns from and a benchmark it is measured against.

Because a model can only learn from work that can be scored, an equally important part of the job is defining what "done correctly" means. Alongside each task you will write the objective and the steps you took, and specify how the result should be verified, what the finished part, sketch, assembly, or drawing has to satisfy for the outcome to count as correct, and which intermediate checkpoints a partially-successful attempt would hit. In CAD this is a sharper question than it looks: a part can be dimensionally correct and still be wrong, because the sketch is under-constrained, the feature order won't survive a parameter change, or the model references a face that will migrate the moment anything upstream is edited. Encoding that distinction, dimensionally right versus actually right, is the contribution we need from you.

This position is with SME Careers, a fast-growing AI data services company and subsidiary of SuperAnnotate that provides AI training data to many of the world's top AI companies and foundation model labs. Your work will directly help improve the world's premier AI models.

## What You'll Do - Author production-realistic CAD tasks: Write task briefs reflecting work an engineering team would actually assign, model a part to a dimensioned drawing, build a parametric family driven by a spreadsheet, assemble components with mating constraints, produce a TechDraw sheet with sections and tolerances, prepare geometry for manufacture or export, scoped tightly enough that the outcome can be objectively verified. - Record expert demonstrations: Set up your own environment for the task (reference drawings, source files, workbenches and add-ons in place) and record yourself executing it end to end with the provided capture tooling, producing a complete trace of actions, interface state, and file changes.- - Document the workflow step by step: Log the steps you took in a form another engineer could follow and reproduce, in clear professional English. - Define verification and success criteria: Specify what the finished model must satisfy, dimensional correctness, fully-constrained sketches, robust feature references, a tree that survives a parameter change, plus the intermediate milestones that mark real progress and the plausible wrong paths that should not be credited. - Cover the range of difficulty: Produce tasks from single sketch-and-pad operations up to long, multi-step workflows that cross applications (a drawing opened from a PDF, dimensions pulled from a spreadsheet, output handed to a slicer, mesh or STEP data round-tripped from another tool). - R

Responsibilities

• Develop AI Training Content: Create detailed prompts in various topics and responses to guide AI learning, ensuring the models reflect a comprehensive understanding of diverse subjects.
• Optimize AI Performance: Evaluate and rank AI responses to enhance the model's accuracy, fluency, and contextual relevance.
• Ensure Model Integrity: Test AI models for potential inaccuracies or biases, validating their reliability across use cases.

Open to experts in

Bangladesh, Bhutan, Brazil, Cambodia, India, Indonesia, Malaysia, Nepal, Pakistan, Singapore, Sri Lanka, Thailand, Philippines, United States, Timor Leste, Vietnam