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The Machine Learning Software Engineering Interview

eng.lyft.com

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Re: The Machine Learning Software Engineering Interview

#21

Post by pmikesell »

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.

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?

Re: The Machine Learning Software Engineering Interview

#22

Post by codingslave »

jorblumesea wrote: post #6
codingslave wrote: post #4

Earlier quoted context omitted.

Problem is its easy to cheat about whats yours

As if regurgitating someone else's leetcode answers is any better? Or googling or stackoverflowing answers? Or a take home challenge? Even at the ML level, engineering is more about applying known good techniques and less about new innovative ideas.

Companies think regurgitating leetcode is better... the number of problems that can be asked is quite large. Memorizing that search space in itself takes tenacity and a good memory...of which are signs of a good employee (regardless of their current coding skill set)

Re: The Machine Learning Software Engineering Interview

#23

Post by thundergolfer »

FpUser wrote: post #17

Summary: we do not really want you to work for us, we are too busy trying to understand WTF do we really want. Amount of BS the article is staggering IMO

I thought it was because I was reading it right as I woke up, but I've re-read it now and it's almost dizzying how it's cloaking the details but written as if it's open and clear.

Re: The Machine Learning Software Engineering Interview

#24

Post by danielmg »

sidlls wrote: post #11
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…

"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.

Does "blockchain" get an honourable mention?

Re: The Machine Learning Software Engineering Interview

#25

Post by gamesbrainiac »

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…

Thank you so very much for saying this. When will these people realise that nobody gives a toss about their overly long and overcomplicated selection process.

And, these guys aren't even Waymo.

Re: The Machine Learning Software Engineering Interview

#26

Post by shantly »

sportanova wrote: post #9

> Most companies are open about the expectations for the role being interviewed for, the interview process, and preparation tips When did this happen? I haven't interviewed for a few years, but nothing was open or clear then

“Most” is certainly wrong. I’d allow “some”.

Re: The Machine Learning Software Engineering Interview

#27

Post by aloknnikhil »

This just reeks of mostly inexperience with a touch of arrogance.

> We looked at other companies’ equivalent roles but they weren’t exactly contextualized to Lyft’s business setting.

Ok, so what does Lyft need then?

> What are Lyft’s challenges (and can a specific role help)? > What should the role be with respect to the organization’s goals? > What are the desired skills, knowledge, and talents given the expectations for the role?

Umm. Isn't that what every company hiring looks to address?

> What’s left is to define the necessary ingredients for what a successful hire looks like vis-a-vis (3) in Lyft’s context: > > Skills acquired through practice, > Knowledge learned through study and personal experience, and > Talents that make each candidate unique.

Ok, I'll stop reading now since "Lyft's context" is no different from any other company let alone one looking to hire a Machine Learning Engineer/Scientist.

Re: The Machine Learning Software Engineering Interview

#28

Post by barry-cotter »

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…

> 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 technical term in philosophy occasionally.

http://www2.csudh.edu/ccauthen/576f12/frankfurt__harry_-_on_...

> One of the most salient features of our culture is that there is so much bullshit. Everyone knows this. Each of us contributes his share. But we tend to take the situation for granted. Most people are rather confident of their ability to recognize bullshit and to avoid being taken in by it. So the phenomenon has not aroused much deliberate concern, or attracted much sustained inquiry. In consequence, we have no clear understanding of what bullshit is, why there is so much of it, or what functions it serves. And we lack a conscientiously developed appreciation of what it means to us. In other words, we have no theory. I propose to begin the development of a theoretical understanding of bullshit, mainly by providing some tentative and exploratory philosophical analysis. I shall not consider the rhetorical uses and misuses of bullshit. My aim is simply to give a rough account of what bullshit is and how it differs from what it is not, or (putting it somewhat differently) to articulate, more or less sketchily, the structure of its concept.

Re: The Machine Learning Software Engineering Interview

#29

Post by sidlls »

danielmg wrote: post #24
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.

Does "blockchain" get an honourable mention?

Definitely.

Re: The Machine Learning Software Engineering Interview

#30

Post by codesushi42 »

sidlls wrote: post #11
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…

"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.

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