Earlier quoted context omitted.
I think you underestimate the level of software architecture and engineering skill that people with formal training in graduate level statistics bring to these jobs. I manage a team of machine learning engineers in a mid-size ecommerce company and I can tell you that the same person who is optimizing Dockerfiles for better layer reuse & figuring out how our CI pipeline will safely get secrets needed to retrieve model…
Your definition of ML engineer comes off as being a high-risk individual to have in an organization. I would rather split those into two separate orthogonal roles and have redundancy in my resource pool. It seems like a very difficult and scare resource to hire. How can Average Corp even think of hiring someone like that? Dead no from me.
There is not a division of labor where one person makes the model and then throws it over the fence to a team that manages its production usage and lifecycle. That team over the fence would not be able to do it, and I’ve seen this attempted org structure fail hard everywhere its been tried for ML services.
What’s telling is that you bring up the personnel risk but you don’t consider the value add. When Average Corp hires someone, it’s because that person brings more value than they cost, period. It’s not because the person is “not risky” in some vacuum of decision making like you’re painting it out to be.
> Dead no from me.
That’s fine and all, but it usually indicates tech death of a company, and likely you have brain drain in more area than just machine learning. If you aren’t willing to take the risk and do what’s needed to structure the work and job to extract value from high performers, that’s a sign of corporate mediocrity and I think anyone with the skill set to be an ML engineer like this would already not even be applying to work in a place like that.
Honestly the risks are even worse than your comment says. There are also big risks around keeping this person intellectually engaged, giving them valuable job experience.
You pretty much have to pay them a lot, give them good work life balance, give them budget for conference travel & continued learning, give them meaningful upward career & compensation growth, and give them meaningful projects.
I look at this and think, yes, if the business can’t give all those things and still be coming out ahead on the person’s productivity, then you don’t want an ML engineer.
But more often the company needs an ML engineer and absolutely would gain more from their productivity than they lose on supporting all those job quality aspects — yet managers just take superficial offense at these demands and balk at the idea that you have to provide meaningful projects and career growth instead of just barking orders and expecting them to put up with work that does not help them grow.