AI annotation
What does AI annotation work involve?
Understand model evaluation, expert annotation and what the work can look like from task to task.
You are usually judging the model, not building it
Many “AI training” roles are human-in-the-loop projects. You might compare two model answers, score one against a rubric, identify a reasoning error, write a stronger answer or create a difficult prompt in your field. You are supplying examples and expert judgement to a larger training or evaluation process—not necessarily coding or training a machine-learning model yourself.
A typical task, from start to finish
First, read the project instructions and scoring rubric. Then review the prompt, check one or more model responses for correctness, relevance, instruction-following, safety and writing quality, and record a score or preference with specific evidence. Some tasks finish with a corrected answer, reference solution, test case or rubric that describes what a strong response must contain.
Where RLHF fits
Preference rankings can be used to teach a reward model which answers people prefer, while human-written examples can support supervised fine-tuning. Other projects are evaluations, benchmark creation, red-teaming or data labelling rather than reinforcement learning. Unless the project says how its data is used, treat “RLHF” as one possible workflow—not a label for every AI task.
What expert projects add
A domain project asks you to apply real professional standards. An engineer may review code, debugging or architecture decisions; a lawyer or clinician may check specialised reasoning; a writer may assess clarity, tone and factual support. The valuable part is not choosing an answer quickly—it is explaining the exact error, trade-off or missing evidence consistently.
What the day-to-day can feel like
Expect qualification or calibration tasks, detailed instructions, repeated work and quality reviews. Guidelines may change between projects, and task availability can rise or fall without guaranteeing a full week of paid work. Contributor reports also describe switching between writing, ranking, rewriting and reviewer tasks, so read the current project brief even if you have done similar work before.
Check the practical details
Confirm eligible countries, required expertise, contractor status, hourly versus task-based payment, which activities are paid, currency and payment timing. Ask about expected weekly hours, project duration, qualification work, review or appeal procedures and what happens when no tasks are available. An advertised hourly rate does not promise a full working week.
Before accepting a project
Review confidentiality and ownership terms, permitted tools and how task quality is assessed. Keep your agreement and payment records. Never paste private client material into a public AI assistant or another third-party tool unless the project explicitly allows it.
These guides are general information, not legal, tax or career advice. Check each platform’s own terms before you apply.