Engineering video
1:06 · 3 October 2026
The speaker explains: staff and principal engineers handle AI work like building harnesses, context engineering at the company level, choosing models, optimizing tokens, and evaluating local or open-source models.
Hear it at 0:00 →And the principal staff, this one is weird because those are the people that are actually focusing on actually the AI work. So this means kind of building harnesses, doing a lot of context engineering at company level, and doing a lot of choosing which models do we use, optimizing our tokens, so there's a lot of work to be done there. And also there's a lot of pressure on all these downstream services.
People are releasing daily now, we used to release every two weeks. And so there's a lot of things that they're bringing under the hood, and the principal engineer side are the ones responsible to to get the company there. One of the good examples I can give is we had this new model that is Jeff, which is basically about adding static safety to LLM generations, to LLM decision making. And that's where engineers, principal engineers come in and like, okay, what are should we use a reasoning model? Can we use a decision making model? Can we use a local model? Can we run an open source model? And all those kind of things.
That I'm not even need to be a machine learning engineer for this, it's the additional engineering. And that's where they would work, whereas back in the days they would be like, oh, should we split this microservice or not? Which is now the kind of work that even seniors can grasp.
