I’m interested in understanding the ML job market for traditional software developers.
Does the opportunity exist to transition into any particular ML roles then grow from there?
Does the opportunity exist to transition into any particular ML roles then grow from there?
For example when I hire MLEs (which I am doing now if anyone wants to apply - supportlogic.io) I am willing to look at people who are solid Python/backend engineers and who have been "ML adjacent" or who we believe could learn the ropes of ML enough to contribute. The stronger an engineer, the more flexibility we have in ML knowledge. Some ML engineering is task-specific but a lot of it is automation, data engineering, and improving data scientist code (for which you do need ML experience
I've found it's a lot easier to teach an engineer enough DS/ML fundamentals to do ML Engineering than it is to teach a data scientist engineering skills. A lot easier...
The other side is deploying it efficiently, and that becomes a more routine software engineering problem. Fundamentally you have some code that you want to run as fast as possible on the cheapest hardware you can feasibly use. Large companies like Google have the luxury of splitting this out into several distinct roles - from pure researchers (people publishing papers), to people who train models for business purposes (eg the Google Lens, computational photography, Translate), to people who optimise the ML library code underneath, to people who build out the end user application with the ML model as a black box service.
Most of those people don't need to know much ML, but the exposure can help you transition into a more ML focused role.