What are the six tasks AI trainers actually do?
1. Rating. You see a prompt and a model answer, and you score the answer for accuracy, helpfulness and safety, usually on a scale with defined anchors. Those ratings become the signal the model is tuned against.
2. Ranking (preference pairs). You get two answers to the same prompt, and you pick the better one and explain why in a sentence or two. This is the most common everyday form of RLHF, and the explanation often matters more than the pick.
3. Rubric grading. This is rating in more detail, where you grade a response one criterion at a time. Did it cite the right standard, show its working, flag the risk, stay in scope? Each criterion passes or fails on its own. If you've ever marked work against a scheme, you've done this.
4. Writing demonstrations. You write the gold-standard answer yourself, whether that's the correct memo, a clean code solution or an explanation pitched at the right level. The model then learns from a right answer instead of only from corrections. In most fields this is the most skilled task type and the best paid.
5. Evaluation and benchmark work. You build or run test sets: you write hard cases, grade model runs against them and measure where performance falls off. Expert pay clusters here, because a benchmark can only be as good as the experts who wrote it.
6. Red-teaming. You try to break the model by drawing out an unsafe, biased or confidently wrong answer and record exactly how you got it. Labs pay for this because a failure found in testing costs little, and one found in production costs a lot.
What does a typical week look like?
Most of it is asynchronous, solo and uneven. You log into a platform, take tasks from a queue in your field and work in blocks you choose. Deadlines are measured in hours or days, not minutes, and work comes in projects, like a model version to grade or a dataset to build. A busy month can be followed by a slow one on the same platform, which is why experienced trainers keep accounts on several.
Your quality is scored the whole time, as a sample of your work gets graded again, and your tier and queue access follow those scores. The pattern holds in every field on Swooply's index. The 1,351 medical roles at a $90/hr median involve different content from the 3,831 language & voice roles at $30. The routine is the same, though: you pick up a task, judge it, write down why and submit.
How is this different from data annotation?
Annotation labels data in bulk, so you draw the box, transcribe the clip or tag the sentiment. Training work judges output. Is this answer right? Is it safe? Is it what an expert would say? Pay follows that difference. General annotation sits at the bottom of the rate range, while judgment work in a field runs at $60–$90/hr medians. For the full comparison, see AI training vs data annotation.
Who becomes an AI trainer?
Mostly people whose careers gave them judgment that's hard to find. Doctors and nurses grade clinical reasoning. Lawyers check citations and arguments. Accountants make calls on recognition and materiality. Engineers review code the way they'd review a pull request, and teachers grade explanations the way they'd mark a paper. There's also a generalist tier that rates everyday helpfulness and safety. Both groups need to read carefully, apply the same standard every time and write a clear reason for each call. Nobody gets hired for "knowing AI." Platforms teach their workflow during onboarding, and what they hire for is the judgment.
What makes someone good at it, and better paid?
Consistency comes first. Your ratings are compared with other experts' ratings and with gold answers, and if you drift, you lose tier. Written reasons come next. Trainers whose explanations actually teach something get sent the demonstration and benchmark work. Then there's depth in a field. The medians by field (medical $90/hr, legal $85, finance $80, science and engineering $70) show where verified judgment is hardest to find. Speed matters least, and rushing is the fastest way to get flagged for quality.
How do you start doing this work?
Pick the field your experience fits, pass that platform's assessment and build a rated track record. Checking 46 platforms one at a time is a slow way to find the right fit. Instead, upload your résumé at Swooply and every live role is scored against it as Strong / Good / Stretch, with pay shown first. It's free for candidates, and your résumé only ranks roles for you and is never sent to platforms. If the tasks are new to you, the free lab at Learn AI training lets you practice rating and rubric work built from your own skills before you take a real assessment.