AI résumé screening scored a 92% match, but the hiring decision was already made
This is the story of a senior candidate who was put through seven interview rounds over two weeks, even though the position had been filled a week earlier. The AI screening rated him a 92% match for the role. But another team's manager had brought in his own person, who was already getting system access. The candidate served as insurance in case the preferred hire dropped out. A week later, he received a rejection email. The story shows that AI in recruiting often masks hiring problems rather than solving them.
AI-processed from Habr AI; edited by Hamidun News
One interview story exposes a problem with AI recruiting that gets discussed less than bias.
The candidate: Misha
Misha is a senior engineer with a solid résumé. It passed AI screening with a "92% match for the position", and the company was proud of its smart ATS. Then came the screening call, a technical interview, system design, live coding, a culture interview, a conversation with the director, and a final round. Two weeks of vacation days. He solved a puzzle about elevators, drew diagrams, and explained five times why he left his previous job.
What Misha didn't know
The position had been filled a week before the final round.
Vadim, who runs a neighboring team, had brought in his own person: "We worked together for three years, I vouch for him." That candidate simply had tea with the director, and that was his whole interview. He was already getting access to systems while Misha was rehearsing his answer about conflict situations.
Misha kept being run through the rounds anyway. Why?
1. Reporting: the funnel, stages and scores have to be in the system 2. Insurance: in case the preferred hire drops out, a counteroffer arrives, or anything else happens, Misha waits on the bench
Nobody told him.
The role of AI
The AI screening worked perfectly: a 92% match. But it did not solve the hiring problem. It masked it. The system says, "Here is a seemingly objective selection process," while the real decision has already been made behind closed doors.
Vadim's person comes from an entirely different stack. He got up to speed in a couple of months and works fine. His experience had no relevance to the position.
What this means
AI in recruiting often becomes cosmetic. Instead of assessing candidates fairly, the system creates an appearance of objectivity while the real decisions are made through closed channels: referrals, internal candidates, connections. The AI takes the blame for the rejection but plays no part in the choice.
Misha's story is not about algorithmic bias. It is about AI often becoming a facade that the company needs more for reporting than for fairness.
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Key Questions Answered
What are the key details on The candidate: Misha?
Misha is a senior engineer with a solid résumé. It passed AI screening with a "92% match for the position", and the company was proud of its smart ATS. Then came the screening call, a technical interview, system design, live coding, a culture interview, a conversation with the director, and a final round. Two weeks of vacation days. He solved a puzzle about elevators, drew diagrams, and explained five times why he left his previous job.
What Misha didn't know?
The position had been filled a week before the final round. Vadim, who runs a neighboring team, had brought in his own person: "We worked together for three years, I vouch for him." That candidate simply had tea with the director, and that was his whole interview. He was already getting access to systems while Misha was rehearsing his answer about conflict situations. Misha kept being run through the rounds anyway. Why? 1. Reporting: the funnel, stages and scores have to be in the system 2. Insurance: in case the preferred hire drops out, a counteroffer arrives, or anything
What are the key details on The role of AI?
The AI screening worked perfectly: a 92% match. But it did not solve the hiring problem. It masked it. The system says, "Here is a seemingly objective selection process," while the real decision has already been made behind closed doors. Vadim's person comes from an entirely different stack. He got up to speed in a couple of months and works fine. His experience had no relevance to the position.
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