The Machine Learning Software Engineering Interview
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Re: The Machine Learning Software Engineering Interview
#2It would be great if companies would allow you to bypass the code challenge if you grant them read git access to a relevant project that you have ownership of.
Re: The Machine Learning Software Engineering Interview
#3Enjoyed the post but I found this sentence interesting.
>As a candidate, it’s easy to get a foot in the door and be evaluated by an interviewer.
Do people really feel that way about a ML SWE postion at Lyft? I would be interested to see what percent of applications they receive make it to the door.
Re: The Machine Learning Software Engineering Interview
#4It would be great if companies would allow you to bypass the code challenge if you grant them read git access to a relevant project that you have ownership of.
Problem is its easy to cheat about whats yours
Re: The Machine Learning Software Engineering Interview
#5It would be great if companies would allow you to bypass the code challenge if you grant them read git access to a relevant project that you have ownership of.
Problem is its easy to cheat about whats yours
Not much harder than with a take-home challenge. Either’d be tough to do convincingly.
Re: The Machine Learning Software Engineering Interview
#6It would be great if companies would allow you to bypass the code challenge if you grant them read git access to a relevant project that you have ownership of.
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.
Re: The Machine Learning Software Engineering Interview
#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.
Having worked in teams with Data Scientists and "ML Engineers" it's been murky whether me and others in my team that were not the former were Data Engineers, Backend Software Engineers, "ML SWE", or "Software Engineer - Machine Learning".
This area of software is rapidly developing and there's no semblance of the bright line that tends to exist between Backend Engineer and Frontend Engineer for ML-involved engineers.
I'm personally interested in moving into the "Software Engineer - ML" space (or is it "ML SWE"?) and thus I have to find out what the right balance between Software Eng skills and researcher skills is. I think I have a decent sense of real-world ML basics but would quickly flounder when pressed for details on mathematical technicalities of different modelling approaches.
Lyft's job listings have these items for "Software Engineer - Machine Learning":
> 5+ years (or Ph.D. with 2+ years) of industry or research experience developing ML models
This really seems like the purview of a research scientist or a data scientist, if I understand the meaning of "developing" correctly.
> Proven ability to quickly and effectively turn research ML papers into working code
This seems fair enough, but my experience has been that the data scientists are doing this mostly, while the "Software Engineer - ML" people are preparing data pipelines or batch training systems.
> Deep knowledge of ML libraries like scikit-learn, Tensorflow, PyTorch, Keras, MXNet, etc
I know it's a job ad and thus it is a 'wish list', but this seems unreasonable.
Guess I'll just keeping studying 'all the things'. Brb in 5 years.
Re: The Machine Learning Software Engineering Interview
#8This 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 interviewing and being interviewed and its process.
Too much obfuscation of ideas that can be very simply explained.
glorification of simpler problems into heroic challenges.
Delusions of Grandeur.
Today's such jobs are tomorrows layoffs.
I think I will stop here. I have crossed my negativity threshold for the day.
Re: The Machine Learning Software Engineering Interview
#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
Re: The Machine Learning Software Engineering Interview
#10This 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…
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