I’m trying to update a job posting so that it’s not complete BS and deters juniors from applying... but honestly this is so tough... no wonder these posting get so much bs in them...

Maybe devRant community can help be tackle this conundrum.

I am looking for a junior ml engineer. Basically somebody I can offload a bunch of easy menial tasks like “helping data scientists debug their docker containers”, “integrating with 3rd party REST APIs some of our models for governance”, “extend/debug our ci”, “write some preprocessing functions for raw data”. I’m not expecting the person to know any of the tech we are using, but they should at least be competent enough to google what “docker is” or how GitHub actions work. I’ll be reviewing their work anyhow. Also the person should be able to speak to data scientists on topics relating to accuracy metrics and mode inputs/outputs (not so much the deep-end of how the models work).

In my opinion i need either a “mathy person who loves to code” (like me) or a “techy person who’s interested in data science”.

What do you think is a reasonable request for credentials/experience?

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    See I'm confused what you are looking for.

    Do you need someone to handle the tech side and be of assistance in the ML, or to handle the ML and assist in the tech?

    This should answer your question as to the skill set your more preferencing.

    It sounds like your leaning more towards the tech side to me, and as such I would advertise the role with tech in mind but add pointers that they will be expected to have an interest in ML.

    Or do we just hire @NoMad and everyone wins
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    @C0D4 hiring @NoMad is always a safe bet
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    "I'm not expecting them to know any of the tech" then are you even hiring an ml engineer? You need a junior dev who likes to work with data. If I take that job and I'm not designing ml models and working on actual ml shit, I'd be pissed.

    Based on what you said, you need a junior dev who:
    - can find their way around debugging docker containers
    - knows what a REST API is
    - knows what continuous integration is (or specific tech you're using for ci)
    - can find their way around data pre-processing

    None of it is specifically an ml engineer's work. You don't mention keras, you don't mention tensorflow, you don't mention scikit, you don't mention pytorch, you don't want someone who knows what learning really is. This is a basic graduate level job, for someone who knows a bit about data science.

    But that's just my two cents. ¯\_(ツ)_/¯
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    @Elyz hiring me won't be a safe bet cuz I'll end up making them a janitor/secretary/maid robot 😂😂 I really should stick to research if this is what the ml eng market is looking like rn.
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    @C0D4 @NoMad in my company an ML engineer is different than a data scientist. The job mostly involves doing dev ops stuff for the data science team, optimizing containers and managing/developing ETL pipelines. The candidate should be ideally able to also chat with the scientists to know if their containers need GPU support and what/why they need some fields.
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