Ask HN: Has anyone made the transition from app development to ML/AI work?

From my experience, there are plenty of teams in FANG that will hire you as a backend developer in a ML team assuming you can pass their interviews. 90% of the work in these teams is not core ML and is more mundane work supporting these models, such as data piping, cleaning, feature generation, experimentation, and real-time serving. You'll get plenty of experience in working directly with ML systems.

The jump to core ML is a bit trickier. Competing with people with PhD's is a drag. Wish people could also give me some tips there.

I am in the process of making this transition now.

I joined Grab.com on their Safety team and started working on their face recognition technologies. This got my feet wet in ML. Now I am leading their content moderation efforts.

TL;DR: Find an "ML adjacent" engineering role and take on ML/AI work.

"ML adjacent" roles could be, content moderation, safety, ads, and search.

The 'field' is rather big, and as with most tech fields, 80% of the work is the same (sometimes mundane) stuff. Infrastructure, CRUD, business logic, the whole 12-factor thing.

Where it would start to get tricky is if you have to do more than 'consume' ML libraries. Everyone can learn how to use a library or API, and getting some training going isn't all that hard either. But if you have to build said library, or come up with a new modelling method, that's where it's a real transition and gets really hard to simply 'switch'. It's also one of those areas where a PhD really helps, not from a "certification-as-entrypass" perspective, but because this gets down to hard science. For most companies, however, that's a point they never reach.

As long as you're interacting with it at a decently high level it's honestly really easy code.

I watched a friend of mine who went from maybe some python courses to writing some really impressive ML stuff as her first project within a few months and with some help from some people who know their stuff a bit, which I found pretty impressive. I think as long as you're building things on top of what's available out there that you can find tons of utility in all the solutions that have been coming up in the past years without a ton of effort. Try dipping your toes into something simple like object recognition and you'll find it's pretty easy.

If you're talking about getting into the field on a level where you're actually developing these technologies themselves then I hope your math is around college-level. Reading deeper into the docs of the tools I'm using and they're showing calculus and linear algebra to me. I don't pretend to understand it very well.

How different is the compensation between typical backend eng and backend ml eng? Not including designing the model itself as that seems to be the domain of phds it seems