Future of ML/deep learning career
I'm currently working as an IC. Got a good offer as applied ML engineer. I'm working on distributed systems right now. In my future job I'll be working as applied ML engineer productizing ML research work produced by research team. How do people on blind see the future of this kind of work. Do you guys this it just as a hype? Current TC 130k Future TC 200k
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How research has no future in ML/AI! I think researchers are most valuable than Software engineers in ML/AI field . The problems in ML/AI can be solved only by new researches and trials . Programming is not that much in ML/AI . And of course ML/AI engineers have a good future. I encourage you to go on ML/AI engineer.
What is the difference between ML engineer and AI engineer? Sorry too many terms keep popping up these days and there is no industry standard as which role maps to what responsibilities.
I am sorrry I will update it . I mean ML/AI . But for your question. AI has many tools and techniques to be achieved , one of them is ML. Also ML has many tools and techniques , one of them is Deep learning . Iam not ML/AI engineer but I read some materials like deep learning.ai . It is good source to learn about ML/AI.
u see how testing jobs became obsolete and became part of development ? as the ML research gets more diluted with more researchers, ML eng will become part of research and will lose value
Not true. ML eng will become (is already becoming) part of software eng as more products and companies adopt ML. Then ML specialty becomes a nice have, if not must have, for software engineering. The same way that distributed systems and scalability is becoming more and more important. This can only be too good for engineers with ML expertise.
Don’t the “testing folks” just become the devs at that point?
I got your point of view . It is reasonable. So what is your advice for software engineer if he wants to specialize in something valuable?
How is ML engineer different from software engineer in machine learning.?
2nd the opinion that ML engineer is better than ML scientist. As an ML engineer you have (should have, can convince people you have) desirable back up skills. Usually ML scientists don’t have (or it’s difficult to convince people they do have) the suit of SWE skills, eg production tools debugging, PR reviewing, api glueing, leetcoding. Also that ML PhD is going to get you the side eye by recruiters and hiring managers
What makes those SWE skills more desirable? It feels like those can be picked up by any ML scientist, but may be harder for SWE to publish papers
It's the other way around actually. SWE can pick up ML skills and try random shit with approximately similar level of success.
For the people who do research, no. For you, yes.
So general SWE role is better than ML engineer?
This wasn't a yes/no question post, so I don't understand whatever Uber rambled on.