What the work involves
You review AI outputs about games and judge whether they hold up against real competitive play. Tasks vary by batch, but commonly include rating two model responses to a strategy question (draft priority, build order, aim routine, macro decision-making) and writing a short rationale for the winner; flagging outputs that sound fluent but describe patches, metas, or mechanics that no longer exist; correcting terminology and rank-appropriate advice; and occasionally writing prompts or reference answers that a model should be able to match. Some batches involve annotating gameplay clips or transcripts — labeling decision points, mistakes, and what an expert would have done instead.
The hard part is not knowing the game. It is explaining why a line is correct in two or three sentences that a non-player reviewer can audit. Vague praise or "this one feels better" gets returned.
What the platform screens for
- Verifiable competitive history — peak and current rank, account handles, team or tournament results, VOD or stream archive, or a documented high-hour record in a specific title.
- Depth in one or two games, not shallow coverage of ten. Screens typically pick your strongest title and press on patch specifics, matchup theory, and edge cases.
- Currency. Whether you still play the current patch matters more than a rank you held three years ago.
- Written clarity in English, since your reasoning is the deliverable.
- Honest calibration — saying "that depends on rank" or "I don't know that matchup" scores better than confident invention.
Logistics
Fully remote and asynchronous. Work is project-based: you are onboarded to a pool, then notified when batches in your titles open. Volume is genuinely uneven — some weeks bring 20+ hours, others none. Most contributors treat this as supplementary rather than primary income. Expect a screening interview conducted largely by micro1's AI interviewer, followed by a paid or unpaid calibration task before live batches. Pay band is as observed on this listing and can differ by project, title scarcity, and task type.