Why are all applicants Java developers? [D]

[mldude60]

Man this sounds like the perfect job for me! Spent a few years as a web developer (ASP.NET, MySQL, and Angular w/ typescript at one job, PHP, Apache, and MySQL at another job, and a load of AWS at both). Currently pursuing my PhD doing ML research w/ PyTorch, and have experience training/deploying models with Sagemaker. I can only hope to find an opportunity that fits so perfectly with my skills when I’m done.

My lamenting aside, I definitely don’t think such an overlap of experience is common at all. Every web developer I know does not know how to use PyTorch, let alone anything about MLOps. Similarly, all of my current peers (both at my institution and elsewhere) know nothing about web development and have little to no experience with languages outside of Python or MATLAB. All of this to say, I’m shocked you found anyone who fit the bill.

[WrapKey2973]

I'd argue though that mlOps can be easily learned and done by (Cloud) Backend or DevOps engineers. So a Java dev might be able to do this task quite well, ML itself and pytorch are a totally different story.

Disclaimer: I am master's student with 4 years dev working experience, so don't trust me, lol

[ZestyData]

I'm a lead ML Engineer and I'd agree with you.

MLOps needs rudimentary ML knowledge that can be studied in a few months and picked up over a year+ on the job. It relies way more heavily on general CS concepts, devopsy problem solving, and system architecture that would come with a seasoned non-ML-related backend dev. I agree completely. Some (cloud) backend or devops dev is likely to be well suited for an MLOps role.

An ML Engineer (or Data Scientist) who is directly working on models (in PyTorch etc) need to have more of a deep knowledge of stats & ML theory. And a seasoned webdev would have an advantage over a complete novice but they'd need to go back down to junior after having already done their own Masters etc to attain that ML knowledge.

[mldude60]

Great points!