Interview behavior is certainly a reflection; presence of a whiteboard, on the other hand, tells you absolutely nothing about the quality of the workplace.
>Are you really offering an easy setup to the senior devops person? I'm sure they can figure things out.
I'm sure a mechanic would figure out their broken car; it just is not ideal to do so everyday on their way to their new job/course.
Plus, in this case, it is not only about mechanics (devops). The window jams and doesn't close when it's raining. The fuel the car can fire changes every week. The seats were salvaged from a 1920's car. Seat belts are too tight and thin. The pedals are slippery. The sequence to turn on the car is in their colleague's head, and he's often absent. The tyres start skidding on certain streets. The door handle are the house door's handle and they must transfer them back and forth every time they take the car, and the refrigerant liquid is leaking, but there's a funnel on the dashboard the driver has put an upside down bottle to automate filling canceling the leak.
Offering them a car that just works is in no way doubting their competence, but merely a catalyst for the change in state they want to happen. Consider it reducing the activation energy.
I gather from your other comment you have a couple of years of experience in machine learning. I suppose with real deadlines and money on the line, with colleagues working on the same project? Can you tell us more about your workflow? How do you deliver value without dealing with jammed windows and leaky reservoirs? Or how do you deal with that?
What's lacking? What's getting in your way? Why does the value take so long to reach end-users?
Something to keep in mind - experienced devops and security experts are extremely valuable, at the same time ML beginners are in oversupply. Do you really want to put yourself at a massive disadvantage in the near to medium term, especially if the future of ML is kind of uncertain while with your other skillset you can have a pretty stable career?
As someone working in ML (a couple of years of experience), I'd much rather be in your position than mine.
There's a huge oversupply everywhere until expert ML levels.
Just about everybody was able to tweak some parameter in models, some can explain themselves, others can't, the end result doesn't differ that much.
The way I see it, there's 2 ways you can walk around this. By being a software engineer that deals with the underlying ingestion/infra (that's increasingly a solved problem too), or be the guy that actually write the ML package themselves. Only the latter will be anywhere near secure, but that's almost 0 percent of the current supply.
I'm glad I got out of machine learning/data engineer role for a pure software engineer.
Managing the resources on the planet in a way that every human being has good quality of life and doing this in a way that's sustainable in the long term and without resorting to oppression, dictatorships or active population reduction.
I read the post and still have no idea what point it's trying to convey apart from a general sense of smugness.
> At the end of my programming day, I want to look on something that is beautiful. I don’t particularly care about how useful a chunk of code is or how much money it might make, or what silly little business problem it solves. If the damn code is ugly I don’t want to see it.
As much as I like 3blue1brown, watching those videos is not a good way to learn. It's maybe a good way to get some intuition about a topic and much closer to the "infotainment" category. A good textbook + solving problems + maybe writing code works much better for actual learning.
Simply switching devices helps. If it's after 5pm and I don't have any meetings or tasks that I can complete by 6pm, I turn off the work laptop and put it away until the next morning.
That's so true. It sucks a bit when you develop a lot of 'online' hobbies, like gaming, reading blogs, etc. But backpains will make even the most hardcore games into runners :)
