Machine Learning Engineer
topic on 6 shows · 9 statements across 8 episodes
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9 statements about Machine Learning Engineer, every show
Fortuna: Domain experts cannot replace ML engineers in LLM fine-tuning
“I think the domain experts, like in our case, clinicians, they're really good at like debugging model outputs, meeting with users, distilling that feedback into something actionable, maybe annotating or doing evals, but they don't necessarily have like, you kn…”
Weil predicts ML engineers will soon embed in every product team
“And so I think you're going to want sort of quasi researcher machine learning engineer types as part of pretty much every team because fine tuning a model is just going to be part of the core workflow for building most products.”
Adams: Great AI products require ML engineers building atop foundation models
“Ultimately, you need, like, really great machine learning engineers. Like, that's where it starts. And if you don't have that, then you know, you gotta find it hard to build truly, really, truly great things. You know, so, like, what OpenAI provide what Entrop…”
Fast AI evolution requires hiring for learning slope over specific domain experience
“Making sure that the slope is there is really critical as someone who's really smart, just because the AI field is changing and particularly this year, every week, it's rapidly changing. And so you're going to need someone who you can't rely on their prior exp…”
Scott: Generative AI transition is harder for ML veterans than new entrepreneurs
“I think it's honestly harder for some, Machine learning people than it is you know, for like a brand new entrepreneur who's, you know, just looking for an interesting thing to go do because it is a very different way for a machine learning team to do its work.…”
Delangue: Only 50 to 100 People Globally Can Train State-of-the-Art AI
“I would say, you know, machine learning engineer, and by machine learning engineer, I mean someone who's really building a new architecture for AI models and able to train state of the art models. They are just in my opinion, a few people in the world who has …”
Hoffman: Machine learning engineers spend up to 99% of time organizing data
“So now, you know, you have these great machine learning engineers, and they thought they were going to be spending You know, all their time predicting the future, but it turns out they're spending 95 to 99% of their time organizing the past.”
Hoffman: Google ML engineers spend 95% of their time building models
“As a machine learning engineer, now you can spend 95% of your time predicting the future, which is what you want to do as a machine learning engineer.”
Gross: Bad engineers over-apply AI to problems lacking ambiguity
“Really great machine learning engineers have the same characteristic that really great all engineers have, which is they're kind of lazy, and they want to find the kind of Occam's razor, the dumbest way to fix the problem, and the bad ones want to kind of over…”