Multiplier AI interview questions

I invested a significant amount of time completing assessments and progressing through multiple interview rounds, only to discover at the very end that academic percentages were apparently one of the most important evaluation criteria. Had this requirement been communicated upfront, it would have saved everyone involved a considerable amount of time and effort. Instead, candidates are taken through multiple stages before being filtered out on a criterion that could have been checked during the very first screening call. What stands out most is the hiring philosophy itself. Despite having relevant industry experience, proven skills, practical knowledge, and the ability to demonstrate competence throughout the interview process, the deciding factor appeared to be academic percentages from years ago. It seems that what you achieved in college matters more than what you have accomplished professionally since then. If academic scores are prioritized above current experience, technical capability, problem-solving skills, and the value a candidate can bring to the organization today, then that should be stated clearly from the beginning. Candidates deserve transparency before investing hours in assessments, preparation, and interviews. The process creates the impression that actual performance and expertise take a back seat to historical academic records. That's a questionable approach, especially for experienced professionals who have spent years building real-world skills and delivering business value. My suggestion to the recruitment team: be upfront about your academic cutoffs and screening criteria. If a candidate can be rejected based on percentages alone, then make that decision before asking them to go through multiple rounds. And one final request: please stop wasting both your team's time and candidates' time with explanations that could have been avoided through a simple screening discussion at the start. Transparency is far more professional than discovering hidden requirements after several rounds of evaluation.