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.
The Machine Learning Software Engineering Interview
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Re: The Machine Learning Software Engineering Interview
#32This 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.
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.
Re: The Machine Learning Software Engineering Interview
#33This 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 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 of economics.
You're link is broken for me btw.
Re: The Machine Learning Software Engineering Interview
#34Good 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 that we set up and started hiring for.
Target: We invented a position called a machine learning software engineer.
Input: First, candidates on the ML SWE loop go through Lyft’s hiring review. The review is a regularly scheduled session for a committee to study candidates with an unbiased perspective and decide whether to hire them. Working alongside the review committee is a separate panel of interviewers that provides technical feedback. This feedback is designed to help the committee decide if there’s a fit and, if so, the candidate’s technical level. At first glance, this review process may seem cumbersome. Examining the checks and balances more carefully, however, we notice that they are intentionally introduced to put friction on the hiring process. Having a consistent review committee unifies standards and eliminates bias.
Target: A committee and a group of interviewers evaluate a candidate for fit and technical level.
Input: Despite the what-ifs, being transparent about how we design interviews can improve our interviews. Call it enlightened self-interest: candidates invest time to talk to us and we mutually benefit from learning if there is a good fit. Even if there isn’t an immediate fit, positive experiences build brand and improves candidate sourcing. Maybe the candidate can reapply when the timing is better. Practically, hiring an engineer easily costs tens of thousands of dollars. By showing how we iterate on our interviews, we reveal what we truly care about and how we try to probe at them, hopefully adding to the virtuous cycle for the hiring pipeline.
Target: We don't respect the time of our readers and are hopelessly unenlightened on this fact.
Re: The Machine Learning Software Engineering Interview
#35I 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'
Re: The Machine Learning Software Engineering Interview
#36This 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…
Re: The Machine Learning Software Engineering Interview
#37This 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.
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.
Re: The Machine Learning Software Engineering Interview
#38Good 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…
Input: Lyft’s high-level problems that stem from its business context
Target: We need to route taxi cabs.
Re: The Machine Learning Software Engineering Interview
#39Summary: 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.
When I was trying to decipher it I thought it was written by somebody in a state of delirium .
Re: The Machine Learning Software Engineering Interview
#40Earlier 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.
Nobody talks about operations research today. But techniques that fell under that umbrella, like ARIMA and linear programming are still widely used, and aren’t going anywhere. (And it’s not without some irony that automated bulk time series forecasting is now sold as AI).