Great comments. I agree with your take on what being an ML Eng actually means. Of course this will vary to a degree from team to team and company to company, but I think you still capture it well.
I absolutely think MLEng is important and much needed, but too often under appreciated. Being this half breed part engineer part ML leaves you on a lonely island often in many orgs. The ML managers don't really understand what you do and neither do the engineering managers. It is kind of thankless unless your management really understands your role and appropriately advocates for you.
MLEng is often an engineer who wanted to get into the sexy ML space and since it is in the title it feels cool. Then you realize you're more an Ops engineer who deals with the inane code of many "true" DS/ML scientists. Thankless, indeed.
Especially in the edge / embedded space, MLEng will imply more than just doing ops.
Stuff to do could include:
- Getting a network architecture to run.
- Applying optimization depending on target arch (pruning, quantisation, custom cuda kernels, etc).
- Integrating models (rule of thumb: a product is 95% ordinary code, 5% is ML related).
- Constructing benchmarks, monitoring
I absolutely think MLEng is important and much needed, but too often under appreciated. Being this half breed part engineer part ML leaves you on a lonely island often in many orgs. The ML managers don't really understand what you do and neither do the engineering managers. It is kind of thankless unless your management really understands your role and appropriately advocates for you.
MLEng is often an engineer who wanted to get into the sexy ML space and since it is in the title it feels cool. Then you realize you're more an Ops engineer who deals with the inane code of many "true" DS/ML scientists. Thankless, indeed.