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The Machine Learning Software Engineering Interview

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Re: The Machine Learning Software Engineering Interview

#61

Post by applecrazy »

PeCaN wrote: post #35
starpilot wrote: post #31

I think this airy, self-aggrandizing post full of BS will be very offputting to data scientists and MLE's. Very low information density here.

that sounds perfect for data scientists and machine learning 'engineers'

I'm confused at the animosity towards ML SWEs and data scientists within this thread. Yes, there's a lot of hype/BS surrounding the field, but there's also some very highly technical people making very novel discoveries.

My question is: why the hate? Isn't a machine learning engineer just as valid as any other engineer?

Re: The Machine Learning Software Engineering Interview

#62

Post by goatinaboat »

SenHeng wrote: post #57
codesushi42 wrote: post #30

Earlier quoted context omitted.

Really? Were you around during the dotcom era? Although I'm not old enough, I've heard that OR in the 80s was the same crap.

What's OR?

What's OR?

https://en.wikipedia.org/wiki/Operations_research

Basically a mathematical approach to problems of logistics and scheduling developed first in WW2. Very powerful in the domains for which it was developed but less generally applicable than enthusiasts hoped, leading to the usual “hype cycle”.

If you have a problem OR could solve or just want to fool around with it PuLP is very easy to use https://pythonhosted.org/PuLP/ Of course the ease of use means that it is a commodity skill now.

Re: The Machine Learning Software Engineering Interview

#63

Post by sjtindell »

hcknwscommenter wrote: post #53
bitL wrote: post #32

Earlier quoted context omitted.

Heh, given that I am starting to see more and more companies that offer ML engineers $2-6k/month (before tax), it's starting to resemble gaming industry in all its negative characteristics instead.

I cannot tell from your comment whether 2-6k/month before tax should be considered a lot or a little. I think in the major tech centers that 2-6k/month is quite low for anyone with significant experience (>5 yrs). Do you disagree?

They used the word negative.

Re: The Machine Learning Software Engineering Interview

#64

Post by dcolkitt »

davnicwil wrote: post #12
codingslave wrote: post #4

Earlier quoted context omitted.

Problem is its easy to cheat about whats yours

I think the trick here is have people talk through what it does, how and why (as in what are the tradeoffs, what other approaches could have been taken, etc). You can't fake this. Or to put it better, even if the code isn't yours and you can do this well, it doesn't even matter that the code isn't yours as in doing this you by definition have the skills and knowledge to reimplement it anyway.

Okay, but at this point it sounds pretty much the same as a traditional interview where the candidate discusses their prior experience.

Re: The Machine Learning Software Engineering Interview

#65

Post by joaogui1 »

hcknwscommenter wrote: post #53
bitL wrote: post #32

Earlier quoted context omitted.

Heh, given that I am starting to see more and more companies that offer ML engineers $2-6k/month (before tax), it's starting to resemble gaming industry in all its negative characteristics instead.

I cannot tell from your comment whether 2-6k/month before tax should be considered a lot or a little. I think in the major tech centers that 2-6k/month is quite low for anyone with significant experience (>5 yrs). Do you disagree?

Since he compared to gaming I think he's saying it's low

Re: The Machine Learning Software Engineering Interview

#66

Post by crispyambulance »

dchichkov wrote: post #37
sidlls wrote: post #11

Earlier quoted context omitted.

"Obfuscation" and "delusions of grandeur" are practically synonyms for ML and Data "Science" in this industry. I've been around for a while and I've never quite seen something as over-hyped and hyper-glamorized as these two specializations.

Calm down. Machine learning is a part of software engineering. Like multiprocessing, computer graphics or network protocols. It is here to stay. It is a part of a pallete of algorithms with which one can build software.

ML can be useful, but it is getting too much attention. Far more hype than the value it actually provides in many domains, IMHO.

Yes, I know that there are folks that deal with vast amounts of data with inscrutable relationships where you need fancy algorithms to make progress. But seriously, most problems just don't need it, and many folks would be better off with mastering basic statistics and data analysis.

It's fascinating how far you can get with basic stuff. My favorite? Statistics for Experimenters, by George E. Box. It's like a secret weapon! https://www.amazon.com/Statistics-Experimenters-Design-Innov...

Re: The Machine Learning Software Engineering Interview

#67

Post by baron_harkonnen »

dchichkov wrote: post #37
sidlls wrote: post #11

Earlier quoted context omitted.

"Obfuscation" and "delusions of grandeur" are practically synonyms for ML and Data "Science" in this industry. I've been around for a while and I've never quite seen something as over-hyped and hyper-glamorized as these two specializations.

Calm down. Machine learning is a part of software engineering. Like multiprocessing, computer graphics or network protocols. It is here to stay. It is a part of a pallete of algorithms with which one can build software.

Your comment is absolutely correct but further points out just how far astray data science has become from any meaningful work. This issue is that a huge number of "data scientists" have limited programming ability and nearly zero engineering sense.

As a perfect example of this is the trend in most places I've seen where data scientists strive to increase the complexity of their model (so they can prove how "smart" they are). A huge part of a software engineering education (whether in the classroom or in dev shop) is learning that complexity is the enemy. No engineer would choose a 3 layer MLP over a simple linear regression for an imperceptible improvement in performance.

The additional irony of all this is that a decade+ ago a software engineer who had strong quantitative and numeric programming skills was rare and an elite find. You would have thought that the data science boom would have dramatically increased the number of these people but I find them even rarer.

Re: The Machine Learning Software Engineering Interview

#68

Post by thundergolfer »

barry-cotter wrote: post #60
thundergolfer wrote: post #33

Earlier quoted context omitted.

> not an economist, though he has written inexplicably popular books on economic topics that betray his lack of understanding of economics. "Debt" I think shows a deep understanding of the relationships economics has with history, philosophy, and society. Graeber knows he's not an economist but he's got a point to make and he's not shy about making it even though it says less than flattering things about some aspects…

What point is Graeber trying to make in Debt ? It seems to be “capitalism bad” but that may be too kind to the book’s coherence. On Bullshit https://en.wikipedia.org/wiki/On_Bullshit https://noahpinionblog.blogspot.com/2014/11/book-review-debt... > Now, this may sound a little silly - if someone wrote a book called "Metal: The First 5,000 Years," and then filled that book with stories of war and bloodshed, never fail…

> It seems to be “capitalism bad” but that may be too kind to the book’s coherence.

have you read the book? The book is an exploration, and an interrogation, with so much to learn from that to say that about it seems pretty philistinic.

Maybe you were just summing up the review you linked from Noah Smith. I read most of it, it's a bit meh but Noah doesn't really seem to be trying too much in it. This though: "leftist mood affiliation". That's cheap 'preaching to the choir' language.

If you have a link to a more serious review I'd genuinely like to read it.

Re: The Machine Learning Software Engineering Interview

#69

Post by laichzeit0 »

mlthoughts2018 wrote: post #13
thundergolfer wrote: post #7

> In the context of the modeling onsite, we ask open-ended problems with sufficient business and problem context such that the candidate can clearly identify an ML-based approach to solve it. I'm disappointed the author wasn't more specific about where the line is drawn between "ML SWE" and "Research Scientist"/"Data Scientist" when it comes to the core ML competencies like model selection, evaluation, and design. Ha…

I think you underestimate the level of software architecture and engineering skill that people with formal training in graduate level statistics bring to these jobs. I manage a team of machine learning engineers in a mid-size ecommerce company and I can tell you that the same person who is optimizing Dockerfiles for better layer reuse & figuring out how our CI pipeline will safely get secrets needed to retrieve model…

Your definition of ML engineer comes off as being a high-risk individual to have in an organization. I would rather split those into two separate orthogonal roles and have redundancy in my resource pool. It seems like a very difficult and scare resource to hire. How can Average Corp even think of hiring someone like that? Dead no from me.
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