How to Interview AI and ML Candidates: A Hiring Manager’s Scorecard
A strong AI or ML interview gathers evidence about the work your team needs done. It should help candidates explain their decisions and give interviewers a consistent way to compare them. Start with a focused scorecard, then use realistic discussions to test each criterion.
Define the evidence before interviewing
Choose criteria tied to the role: relevant technical foundations, delivery experience, evaluation judgment, collaboration and ownership. Write down what a strong answer would demonstrate. Use the same core questions across candidates and record concrete evidence instead of relying on general impressions.
Explore one project in depth
Ask what problem the project addressed, what the candidate personally owned and which alternatives they considered. Follow up on constraints, failed assumptions and what happened after release. This separates hands-on responsibility from familiarity with a team’s work and makes it easier to assess seniority.
Use a realistic scenario
Present a bounded problem from the role, such as unreliable retrieval, a model that performs differently on new data or a slow inference service. Ask what the candidate would investigate first, what information is missing and how success would be tested. Evaluate the reasoning and tradeoffs rather than expecting one predetermined answer.
Score independently and make gaps explicit
Have interviewers write their assessment before the group discussion. Distinguish evidence from assumptions and decide whether a gap is essential or trainable. Keep exercises proportionate to the role and time required. Give candidates a clear explanation of the process, responsibilities and next steps; a well-run assessment also helps them decide whether your team is a good fit.
A question for your hiring team
What specific evidence would convince your interview team that a candidate can deliver the first assignment?
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