Interactive Environment Data Collector

We are collecting human gameplay data to train AI agents that operate in interactive 3D environments. You will work through scripted scenarios in various game engines, logging your actions and annotating key decision points. Some sessions require you to play optimally; others ask you to intentionally explore suboptimal strategies so the AI can learn from a broader range of behaviors. Each session produces a replay file and a structured annotation log.

You should be a competent gamer with experience across multiple genres -- FPS, strategy, puzzle, and open-world. We do not need esports-level skill, but you do need to be comfortable learning new game mechanics quickly and playing systematically rather than casually. Familiarity with game engines (Unity, Unreal) is helpful for debugging session issues but not required.

Work is project-based with fixed session windows. You sign up for available session slots through our scheduling portal. Payment is per completed session, with bonuses for annotation quality. Typical commitment is 10-20 hours per week.