@seclink: HARD Non-Code Ultra-Long-Cycle Tasks | Expert Question Designers Recruitment Requirements for This Recruitment 1. No re…

X AI KOLs Following News

Summary

This article details recruitment requirements for expert question designers to develop non-code, long-cycle tasks for AI model validation, stressing authenticity, quality control, and relevant professional experience.

HARD Non-Code Ultra-Long-Cycle Tasks | Expert Question Designers Recruitment Requirements for This Recruitment 1. No restrictions on industry or professional field, but must possess genuine professional, research, educational, or organizational project experience, and have taken on key responsibilities in relevant projects; only apply for scenarios where you truly have actual experience. 2. Ability to transform real complex work into single-round, non-code long-cycle tasks; the primary task objective must not be developing software, modifying code, or troubleshooting program issues. 3. Tasks must have authenticity and integrity: multiple stages revolve around the same goal and are interdependent, with changes in key data, assumptions, or decisions impacting downstream outcomes. 4. Ability to provide legal, authorized, and desensitized attachments such as documents, tables, interview records, business data, project materials, or reference templates. 5. Ability to independently write exam points, passing criteria, hard failure items, common pitfalls, response strategies, standard or reference answers, and corresponding attachments, while specifying the manual review cycle. 6. Ability to cooperate in frontier model difficulty validation, question duplication checks, quality inspections, supplementary materials, and revisions; questions with excessive similarity or failing quality inspections will not be adopted.
Original Article
View Cached Full Text

Cached at: 09/01/26, 01:44 PM

HARD Non-Code Ultra-Long-Cycle Tasks | Expert Question Designers Recruitment

Requirements for This Recruitment

  1. No restrictions on industry or professional field, but must possess genuine professional, research, educational, or organizational project experience, and have taken on key responsibilities in relevant projects; only apply for scenarios where you truly have actual experience.

  2. Ability to transform real complex work into single-round, non-code long-cycle tasks; the primary task objective must not be developing software, modifying code, or troubleshooting program issues.

  3. Tasks must have authenticity and integrity: multiple stages revolve around the same goal and are interdependent, with changes in key data, assumptions, or decisions impacting downstream outcomes.

  4. Ability to provide legal, authorized, and desensitized attachments such as documents, tables, interview records, business data, project materials, or reference templates.

  5. Ability to independently write exam points, passing criteria, hard failure items, common pitfalls, response strategies, standard or reference answers, and corresponding attachments, while specifying the manual review cycle.

  6. Ability to cooperate in frontier model difficulty validation, question duplication checks, quality inspections, supplementary materials, and revisions; questions with excessive similarity or failing quality inspections will not be adopted.

Similar Articles

@svpino: Some companies are still hiring exclusively with old-fashion questions (no AI involved). Some companies (most that I've…

X AI KOLs Following

Some companies are still hiring exclusively with old-fashion questions (no AI involved). Some companies (most that I've seen), are starting to let candidates use AI as part of their interviews. Some companies are exclusively asking questions related to AI-assisted development: harnesses and how to use them, MCP and how to implement or take advantage of them, best way of authoring skills, evaluation techniques, etc. I suspect most companies will eventually move toward the third group. > **Carlos

@DeRonin_: As an AI engineer in 2026, learn this: > systematic output reading. pattern recognition across 1,000 model responses is…

X AI KOLs Timeline

A seasoned AI engineer shares key skills for 2026, including systematic output reading, context engineering, tool description discipline, eval design, model routing, prompt versioning, confidence scoring, streaming architecture, fallback chains, latency budgets, failure cataloguing, agent-vs-workflow decisions, and failure post-mortems as portfolio content.