safety

The user is driving. Every design choice follows from that, and candidates who sketch a chat screen fail the question in the first minute.

An emotionally loaded design question. The interviewer is watching for a product that actually helps, and for the judgment to see where companionship turns into dependency, manipulation or missed emergencies.

Health assistants are useful exactly where they are risky. The interviewer wants clear tiers of what the assistant answers, how it handles emergencies and personal medical advice, and an experience that stays helpful instead of hiding behind disclaimers.

A principal-level strategy question with business, ecosystem and safety dimensions. There is no universal right answer; the interviewer wants a decision framework tied to the lab's business model and the model's risk profile.

Guardrails are often described as a box in a diagram. The interviewer wants specific checks, where each runs, what it costs in latency, and what happens when one fires.

An incident question where the root cause is almost never 'the model made a mistake'. The interviewer wants containment first, honest communication, and a fix to the permission design that made deletion possible.

The security question every agent PM must answer well. Anyone can send your user an email, which means anyone can put instructions in front of the model. The strong answer stops trying to make the model resist and limits what a fooled model can do.

A red team that ends in a slide deck changes nothing. The interviewer wants a plan with scope, people, severity ratings, exit criteria, and a way to turn findings into permanent tests.

AI incidents often have no outage, no error and no deploy: just a model saying or doing something harmful at scale. The plan has to detect those, contain them fast, and learn from them.

Part incident response, part communications, part safety engineering. The interviewer wants a calm sequence: verify, contain, fix the class of attack rather than the one prompt, and communicate honestly.

A principal-level platform design. Customers want better results on their tasks; most of them would get there faster without fine-tuning. The product has to guide them, protect their data, preserve safety, and survive base model upgrades.

Connect the lab's published work to your own product decisions. Prepare an informed view, a real disagreement and the evidence that would change your mind.

A values question at AI labs and regulated companies. Panels want judgment about real risk, a constructive path forward, and courage without self-righteousness.