Spec Trust-and-Safety Eval Harness for an LLM-Powered Customer-Support Bot

Think of this role as the loyal opposition inside an AI lab. While teammates race to make a model more capable, AI safety researchers ask what happens when it succeeds — at the wrong thing, for the wrong reasons, in the wrong hands. The work spans red-teaming prompts, designing constitutional methods that nudge models toward principled behavior, and translating findings into guardrails that product teams can actually adopt. Good work here is rigorous and humble: it admits what's still unknown rather than papering over it. Students grow into this path by pairing technical depth in PyTorch with reading widely across ethics, policy, and security. The field rewards people who can hold both at once.

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