Technical practice
How would you design an AIGC detection algorithm that remains useful as generators change?
What to coverPreparation suggestion: explain how you would assemble representative evaluation data and test generalization to new generators and transformations. Discuss false positives, false negatives, and when a detector should abstain rather than make a confident claim.
How would you combine media forensics with generative-content provenance analysis?
What to coverPreparation suggestion: distinguish evidence in the media itself from available provenance signals, and explain how you would handle missing or conflicting evidence. Discuss what conclusions the system could support without overstating certainty.
How would you evaluate a face-liveness system against attempts to impersonate a real person?
What to coverPreparation suggestion: cover representative genuine and attack cases, error rates, and robustness across capture conditions. Explain the trade-off between blocking attacks and rejecting legitimate users.
What technical approach would you take to deepfake defense beyond detecting a single known attack type?
What to coverPreparation suggestion: describe how you would test across manipulation methods and changing media quality. Address failure analysis and the limits of relying on artifacts that may disappear as generation improves.
How would you connect document understanding to fraud detection?
What to coverPreparation suggestion: describe extracting and checking relevant document content before making a fraud assessment. Discuss uncertain or degraded inputs and the trade-off between detecting suspicious cases and incorrectly flagging valid documents.
How would you set technical direction for a multimodal or vision-language model?
What to coverPreparation suggestion: explain how you would compare candidate approaches on the role’s target tasks, including reasoning quality and robustness across modalities. Relate model performance to production cost and the reliability needed for a trust-related application.
How would you evaluate a multimodal AI agent before using its outputs in a trust workflow?
What to coverPreparation suggestion: discuss how you would test whether its reasoning is grounded in the supplied inputs and whether it handles uncertainty. Explain where constrained actions or human review might be preferable to autonomous decisions.
What makes a biometric-security or trustworthy-AI algorithm dependable in practice?
What to coverPreparation suggestion: consider adversarial misuse, variation in input quality, and error rates across relevant conditions. Explain how you would assess security gains against legitimate-user friction without assuming any particular company policy.
How would you take a promising research algorithm through production?
What to coverPreparation suggestion: describe stages from reproducible evaluation and failure analysis to integration, monitoring, and iteration. Address trade-offs among quality, latency, operational complexity, and maintainability.
How would you make a major algorithmic decision when no candidate solution clearly wins?
What to coverPreparation suggestion: define the evidence and evaluation criteria you would use, including reliability and business impact. Explain how you would document uncertainty, choose a reversible next step where possible, and identify what results would change your decision.
Behavioural practice
Tell me about a time you led an ML system from concept to production.
What to coverPreparation suggestion: use situation, action, and result to show the scope of your ownership and the decisions you made along the way. Explain the outcome with evidence you can substantiate, including any reliability lessons.
Describe a time you mentored an experienced engineer through a difficult technical decision.
What to coverPreparation suggestion: use situation, action, and result to show how you challenged or supported their reasoning without simply taking over. Describe what changed in the decision or the engineer’s approach.
Tell me about a time you aligned an algorithm strategy with product and engineering partners.
What to coverPreparation suggestion: use situation, action, and result to explain differing priorities and how you reached a workable decision. Connect your technical choices to the impact the partners needed.
Describe a time you had to exercise technical judgment with incomplete evidence.
What to coverPreparation suggestion: use situation, action, and result to explain the uncertainty, the alternatives you considered, and why you chose a path. Include what you learned when more evidence became available.
Tell me about a time you prioritized long-term reliability over a short-term improvement.
What to coverPreparation suggestion: use situation, action, and result to explain the trade-off and how you made it visible to others. Describe the eventual effect on the system or the team without claiming an outcome you cannot verify.