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The Machine Learning Software Engineering Interview

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Re: The Machine Learning Software Engineering Interview

#51

Post by lliamander »

codesushi42 wrote: post #30
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.

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.

It's funny but at my last company, one of our systems used some linear programming to generate a model of physical processes.

The problem could have been tackled with greater accuracy using machine learning, but it would have taken a long time for the system to generate enough data points for a sound model and would have required more storage space. This was also complicated by the fact that the model had to be regenerated whenever the physical system being modeled was changed.

The linear programming solution was a lot cheaper and was "close enough" to serve as a useful approximation.

Re: The Machine Learning Software Engineering Interview

#52

Post by dtech »

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.

Yes! It's one tool in the software engineering toolbox! It's a great tool for some problems!

Due to the hype it becomes a goal in some organizations however. "We need to do machine learning because we have big data" or some such. Doesn't matter if the problem could've been solved in 5% of the time and cost with 20 lines of code, thou shalt use machine learning.

It doesn't help that data scientists (creating and training the ML model) and software developers (creating and maintaining the software) usually come from different backgrounds, requiring a "data engineer" as an additional intermediary.

It always a problem with hype, blockchain (or merkle trees) has the same problem but worse, because the problems it solves well are rarer and more narrow.

Re: The Machine Learning Software Engineering Interview

#53

Post by hcknwscommenter »

bitL wrote: post #32
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.

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?

Re: The Machine Learning Software Engineering Interview

#54

Post by hcknwscommenter »

m0zg wrote: post #49
yanilkr wrote: post #8

This blog post was so painful for me to read. This is a symptom of "bullshit" going on around in big tech companies. "bullshit" here is an economic term defined in the book "bullshit jobs". https://www.amazon.com/Bullshit-Jobs-Theory-David-Graeber/dp... Reading through the post, I was noticing So much corporate Jargon which really does not mean anything important. Dehumanizing language when describing people intervie…

Yep. Taxi routing service, and not even the best one, you'd think they're launching those taxis to Mars. That said, SpaceX interview process is even more ridiculous. The first step is to talk on the phone with a non-engineer recruiter who has to ask you highly technical questions, but doesn't understand a word of your response, and you know it. They then sort of have to correlate what you're saying with the answers t…

Not saying this is the case for spacex, but in my field (totally not space or engineering or software (but very much "tech" (physics/chemistry)) related), these types of interview are for weeding out the non-standard folks (of which there are many, including me but many of us (including me) have become good at hiding it). A person with high E would presumably (but not always because it is indeed a difficult task, casualties are regrettable but expected) "grok" the task and begin feeding the right keywords to the recruiter. Once you realize the game, it becomes fairly easy. Just read the job description and sprinkle the keywords provided therein.

Re: The Machine Learning Software Engineering Interview

#55

Post by hcknwscommenter »

starpilot wrote: post #34

Good source of data for training a de-bullshitter. Input: A year and a half ago when we began scouting for this type of machine learning-savvy engineer —something we now call the machine learning Software Engineer (ML SWE) — it wasn’t something we knew much about. We looked at other companies’ equivalent roles but they weren’t exactly contextualized to Lyft’s business setting. This need motivated an entirely new role…

Left unsaid is the "everyone gets a veto" crap that destroys hiring 10x contributors. I have personally been involved in numerous interviews (as the interviewer ), where my fellow interviewers have deliberately shitcanned candidates because they seemed extremely well qualified and highly motivated (as compared to my colleague). Everyone gets a veto is almost uniform in my field and it is a massive problem.

Re: The Machine Learning Software Engineering Interview

#56

Post by tempodox »

Keloo wrote: post #36
yanilkr wrote: post #8

This blog post was so painful for me to read. This is a symptom of "bullshit" going on around in big tech companies. "bullshit" here is an economic term defined in the book "bullshit jobs". https://www.amazon.com/Bullshit-Jobs-Theory-David-Graeber/dp... Reading through the post, I was noticing So much corporate Jargon which really does not mean anything important. Dehumanizing language when describing people intervie…

https://mourner.github.io/bullshit.js/

mission critical!

Re: The Machine Learning Software Engineering Interview

#57

Post by SenHeng »

codesushi42 wrote: post #30
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.

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?

Re: The Machine Learning Software Engineering Interview

#58

Post by davnicwil »

pmikesell wrote: post #21
davnicwil wrote: post #12

Earlier quoted context omitted.

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.

You can absolutely fake this. There’s a huge difference between coming up with and implementing something vs understanding it enough to be convincing. Have you actually tested this out?

Yes I've done this both as an interviewer and interviewee, it's a pretty valuable exercise I've found. Not a silver bullet, not the only thing you can do, not appropriate for every situation, but generally good.

Regarding your other point I agree with you, but if the main point is to evaluate understanding then the difference doesn't really matter. If you're testing for ability to invent concepts etc, then this might be more important to you.

Re: The Machine Learning Software Engineering Interview

#59

Post by applecrazy »

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…

Undergrad here. This is kind of disappointing.

I really want to work in this field, but it seems I must have a PhD at minimum and not sure if I want to take that step yet.

Re: The Machine Learning Software Engineering Interview

#60

Post by barry-cotter »

thundergolfer wrote: post #33
barry-cotter wrote: post #28

Earlier quoted context omitted.

> This is a symptom of "bullshit" going on around in big tech companies. "bullshit" here is an economic term defined in the book "bullshit jobs". Bullshit is neither an economic term nor an anthropological one. David Graeber is an anthropologist, not an economist, though he has written inexplicably popular books on economic topics that betray his lack of understanding of economics. Bullshit is actually used as a tech…

> 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 failing to remind us after each anecdote that metal was involved in some way, we might be left scratching our heads as to why the author was so fixated on metal instead of on war itself. And in fact, that is indeed how I felt for much of the time I was reading Graeber's book. The problem was exacerbated by the fact that Graeber continually talks around the idea of debt in other ways, mentioning debt crises (without reflecting deeply on why these happen), the periodic use and disuse of coinage (which apparently is just as bad as debt in terms of enabling the capitalism monster), and any other phenomenon related to debt, without weaving these observations into a coherent whole.

> In other words, I am now angry at myself for paraphrasing the book, and trying to put theses into Graeber's mouth, because this is such a rambling, confused, scattershot book that I am doing you a disservice by making it seem more coherent than it really is.

> The problem of extreme disorganization is dramatically worsened by the way that Graeber skips merrily back and forth from things he appears to know quite a lot about to things he obviously knows nothing about. One sentence he'll be talking about blood debts and "human economies" in African tribes (cool!), and the next he'll be telling us that Apple Computer was started by dropouts from IBM (false!). There are a number of glaring instances of this. The worst is not when Graeber delivers incorrect facts (who cares where Apple's founders had worked?), it's when he uncritically and blithely makes assertions that one could only accept if one has extremely strong leftist mood affiliation

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