Algorithm puzzles told the studio nothing about whether a candidate could ship gameplay systems under real constraints.
Assessments used realistic gameplay-style tasks in a live workspace, revealing how candidates structured systems and handled trade-offs.
A gaming studio's work is specific: performance budgets, real-time systems, and messy domain constraints. Generic algorithm tests said little about who could actually do it.
The problem: tests that miss the craft
Puzzle rounds rewarded memorized patterns, not the systems thinking gameplay engineering demands.
- Strong gameplay builders failed abstract puzzles
- Puzzle-passers struggled with real systems
- Interview slots wasted on bad-fit candidates
The shift: domain-realistic tasks
Candidates worked realistic gameplay-style tasks in a live workspace, showing how they structured systems, managed performance, and reasoned about trade-offs.
- Tasks that mirror the studio's real work
- Full visibility into structure and decisions
- AI usage captured and assessed
The outcome
Predictive accuracy rose 52%, the studio extended twice as many confident offers, and bad-fit interviews fell 40%.