What the work involves
You write hard questions and then prove they are correct. Each task typically means one graduate-level single-answer multiple-choice item inside your specialization — analog or digital circuits, RF, power electronics, and especially integrated circuit design — accompanied by a worked explanation, distractor rationale, and a grading rubric that another expert could apply without talking to you. A second stream of work is review: you validate AI-generated or peer-authored items, flag ambiguous stems, incorrect answer keys, and distractors that are defensible under a different but reasonable assumption. You will also evaluate model outputs directly, describing where the model's reasoning chain diverges from how a competent graduate student would actually approach the problem.
The bar is that the item must be unambiguous, non-Googleable, and require real reasoning rather than recall of a formula. Questions that a strong model answers correctly on first attempt are often less useful than ones that expose a specific reasoning gap — but they still have to be fair and textbook-defensible.
What the platform screens for
- Verified credentials and teaching record. PhD or advanced Master's in EE, plus university-level teaching (professor, adjunct, lecturer, or experienced TA). Expect to name courses and levels.
- Depth under follow-up. micro1's screen is AI-led and will push on your stated specialization with progressively narrower technical questions. Broad familiarity across many EE subfields is worth less here than defensible depth in one or two.
- Assessment craft. Whether you can articulate why a distractor works, how you'd handle a question with two arguably correct answers, and how rubrics survive contact with graders who aren't you.
- Written clarity. Solutions are read cold by other experts and by model training pipelines; ambiguity is a defect.
- Realistic availability. Roles typically fill within 48 hours, and first tasks are expected within 24–48 hours of onboarding.
Logistics
Fully remote, contractor engagement, asynchronous collaboration with other subject matter experts. Compensation is output-based — paid per task that meets project specifications — so the effective hourly figure depends on how quickly you author clean items. A weekly minimum submission requirement applies. No prior AI or machine learning experience is required or expected; domain knowledge is the deliverable. Pay bands are as observed on the platform and are not guaranteed.