Interviewer calibration · Core questions

1. 01
   Two reviewers often disagree about the same set of AI outputs. How would you tell whether the issue is annotator disagreement, an unclear evaluation standard, or model instability?

   Why selected Calibrate disagreement in AI engineering evaluations by examining evidence and standards.

2. 02
   A resume line saying "used AI to improve engineering efficiency" carries little information. How would you rewrite it into an experience others can probe and verify?

   Why selected Turn AI project claims on a résumé into verifiable responsibility and evidence.

3. 03
   An AI assistant can create tickets, issue refunds, or change accounts on a user's behalf. A refund request times out, and the system cannot tell whether it completed; retrying may issue a second refund. How would you check the outcome, control retries, and ensure actions stay within the user's authorization?

   Why selected Use duplicate side effects to examine judgment about agent reliability.

4. 04
   Candidate feedback contains contradictory impressions, and interviewers use different standards. How would you improve hiring calibration?

   Why selected Calibrate differing interviewer conclusions against role requirements and observed evidence.

5. 05
   Tell me about a project that did not succeed. Why did it fail, which causes were direct and which more fundamental, and how much of it related to your own judgment?

   Why selected Probe decisions, attribution, and learning in a failed project.

6. 06
   Choose an outcome delivered by several teams that you need to explain in a review or promotion case. What problem did you define, which key judgments did you make, and what actions did you drive? Which contributions belong to others, and what evidence supports your account?

   Why selected Ask candidates to distinguish personal contribution from shared team outcomes.

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