AI Domain Expert

$140–200/hrrate as listed

micro1 contracts domain experts part-time to critique AI-generated outputs, write adversarial prompts, and annotate data in their field of practice. Observed rates run $140–200/hr, with software engineering, finance, data science, and legal backgrounds most in demand.

Summary, eligibility guidance and screening preparation are prepared by Sidequest from the platform's posting. Check the source listing before applying.

What the work involves

You receive AI-generated outputs in your domain — code, technical writeups, financial analysis, legal reasoning — and judge them against the standard you'd apply to a colleague's work. That means finding the error, naming why it's an error, and writing feedback specific enough that a model trainer can act on it. A second stream of work runs the other direction: you author prompts designed to expose weak reasoning, drawing on the realistic edge cases you've actually encountered in practice. Senior contributors also review other experts' annotations and feedback for quality, which is where consistency and calibration matter most.

No AI background is expected. The premise is that your professional judgment is the scarce input; the annotation tooling is learnable in an afternoon.

What the platform screens for

micro1 runs an AI-led interview before human review. Expect it to probe:

Verifiable depth — specific projects, systems, or matters you worked on, with follow-up questions that test whether you actually did the work you described
Written English under load — the ability to explain a nuanced technical judgment in a few tight sentences
Evaluation instinct — whether you can separate an answer that is wrong from one that is merely differently-reasoned, and whether you flag confident-sounding errors
Documented output — a track record of technical docs, memos, research, or design specs is weighted heavily

Logistics

Fully remote, contractor, part-time, asynchronous. Most contributors work in blocks around an existing full-time role; project volume fluctuates, and there is no guaranteed weekly minimum. Pay bands are as observed on the platform and vary by domain, seniority, and project — treat $140–200/hr as a range, not a commitment. Onboarding typically includes a paid or unpaid calibration task before live work begins.

What they look for

data annotation
prompt engineering
critical thinking
written communication
domain expertise
feedback delivery
remote collaboration
attention to detail
adaptability
ethical awareness in ai
Ai output evaluation
Quality Review
professional writing

Before you apply

Would you pass this screen?

Drawn from what this listing actually requires. Answered in your browser — nothing is sent to us, and we do not store your answers.

Do you have 3 or more years of professional experience in software engineering, finance, data science, law, or a closely related analytical field?
Domain expertise · required
Do you write in English at a professional level and can you demonstrate this with work samples?
Written English fluency · required
Are you able to work independently in a remote setting with access to a reliable internet connection?
Remote work capability · required
Have you worked on data annotation, prompt engineering, or evaluation of AI outputs—or a similar domain-specific quality review role?
Data annotation or AI evaluation exposure · preferred

0 of 4 answered

Included with this listing

Interview prep — 10 questions

Matched to what this platform's screening actually measures. Answer each out loud in 60–90 seconds: claim → concrete example → implication.

What it tests: Whether the candidate can deliver specific, actionable critique rather than vague praise or blame—a core skill for evaluating AI outputs and annotating data reliably.
A strong answer: The answer should name a concrete scenario (e.g., a code review, a memo, a proposal), describe what was wrong or unclear, and show how the feedback was framed to be constructive and precise—focusing on the work itself, not the person.
Where people lose it: Candidates often give only surface-level feedback ('it was good but could be better') or describe their own corrections rather than the critique process. Avoid saying you gave feedback without showing its specificity or how you prioritized what to flag.