Safety and security

AI robustness, safety engineering and incidents

This family covers the work of testing how AI systems behave when conditions change, and of handling the failures that cause real harm. AI companies need it because a model that works in a demo can still fail badly once customers depend on it.

More AI systems now sit inside products where a failure costs customers, not just accuracy points. Founders feel that pressure before they have a team to answer it. The hires are tricky because the talent is thin and the titles are inconsistent. Strong candidates often come from reliability, safety engineering or risk, and have to learn how models fail. Others come from applied ML and have to learn how safety evidence is built. Few have done both.

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