Imagine you're part of a team deploying a conversational AI model that can reason across sensitive topics. During internal testing, you discovered that the model usually gives overly confident but factually wrong answers in high-risk contexts. How would you investigate and mitigate/solve that problem?

"I applied through Anthropic's site and went through a recruiter screen, a technical assessment, another technical round, a panel, and then a final onsite-style step that for me was two more technical tasks. A lot of the process centered on SQL and Python data work, first pulling data, then cleaning and analyzing it, but the ethics questions were way heavier than what I usually see. The panel felt like a work simulation about deploying a conversational AI model safely in high-risk settings, with different interviewers pushing on misuse, alignment and credibility, and privacy. My final round was a Python data-transformation task plus a debugging exercise on a prewritten pipeline, and the debugging is where I ran out of time. I did not make it to the hiring manager conversation after that, so I was rejected at the final technical stage."