What the work involves
This is not shop-floor labor. You're translating hands-on machining judgment into structured training data: writing step-by-step setup narratives, diagnosing a described chatter or dimensional-drift scenario and explaining your reasoning, or reviewing a model's answer about work-offset procedure and marking exactly where it goes wrong. Tasks are typically drawn from real production situations — a first-article that came in 0.003" oversize, a thin-wall aluminum part deflecting under a roughing pass, a tool change that shifted Z on a Carvera.
A typical block of work might include:
- Writing detailed process walkthroughs for a part given a drawing with GD&T callouts
- Comparing two model-generated machining approaches and justifying which is manufacturable
- Flagging answers that are technically plausible but would scrap a part or damage a spindle
- Documenting inspection method choice — when a caliper is adequate versus when you need a micrometer, indicator, or CMM
What the platform screens for
micro1's screen is AI-led and conversational. It probes depth: expect follow-ups that go a layer past your first answer, asking for specific machines, controls, materials, tolerances, and what actually happened. Vague experience claims collapse quickly. Reviewers care that you can distinguish a real cause from a superficially similar one, and that you can write clearly enough that a non-machinist annotator could follow your logic. No AI or ML background is expected or required.
Logistics
Fully remote and largely asynchronous. Contributors generally set their own hours against task batches, with volume varying by customer demand — some weeks are steady, others are thin. Engagements are contractor-based, and the $50–100/hr band reflects rates observed on similar micro1 listings rather than a guaranteed offer; actual rate depends on screening outcome and task tier.