
Ask HN: How was your machine learning interview at Google/Facebook/Amazon - thro1237
Looking for feedback from folks who applied for Machine Learning engineer or related positions at Google&#x2F;Facebook&#x2F;Amazon. How was your interview? How focused was the interview on ML topics as opposed to coding? How big was the emphasis on Deep Learning as opposed to more traditional methods? Was the interviews math heavy?
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googmlint
I recently interviewed for a machine learning position at Google. There was
essentially no focus on machine learning. I had 6 interview sessions total (5
onsite and 1 live coding phone screen) and 5 of those interviews had nothing
to do with machine learning. In fact, those 5 interviewers had no machine
learning background and knew nothing about machine learning. The one interview
that was focused on machine learning was fairly straightforward and simple. It
was kind of funny how large a contrast there was between the Google
recruiter'a pitch ("we want to become an AI first company") and my actual
experience interviewing. The interview process I went through has no way of
distinguishing between someone with in depth machine learning knowledge and
someone with basic machine learning knowledge. It was essentially a software
engineer interview. I did not get the position, but I would recommend you look
elsewhere if your focus is truly machine learning

~~~
apohn
>I did not get the position, but I would recommend you look elsewhere if your
focus is truly machine learning

For ML, it's really important to join a team where your interests in ML are
aligned with what the team does. It's really hard to see this from a job
description. There's enough going on at Google that you can find work that
fits.

I've interviewed twice at Google and had the same experience as you. No ML or
math questions at all. More algorithms and how to quantify a business problem.
That being said, I asked enough questions to realize that some groups use ML,
but that's a small part of what they are doing. For example, they might have a
platform for doing A/B testing and the "Data Scientist" job is really defining
A and B and feeding that into the platform to extract metrics. How much ML
being done is going to be different on the Ads team than a customer facing
services role for Google Cloud.

I had similar experiences interviewing at Facebook, just with more probability
and stats brain teasers. Facebook doesn't guarantee which job you'll get once
you're in. You go to the bootcamp and then which team you end up is decided
after the bootcamp. That doesn't work for everybody if there are certain types
of ML work you're not interested in.

~~~
isuckatcoding
Wow that sounds like such a boring job.

~~~
apohn
For the the type of role I described in my post, being able effectively define
metrics to quantify a business problem (e.g. how can we better engage with
group X on our platform) and work with product engineering to build those
features (including helping to define the data collected and how it's stored)
into the platform is more important than tweaking a bunch of parameters in a
machine learning model.

A lot of those folks are not only thinking of ways to quantify a business
problem, they are actually thinking of new business problems (e.g. what does
it even mean to "engage" on our platform). It can be quite creative and
challenging.

Unless you are writing the core algorithms or working as a statistician, a lot
of ML jobs are some variation of the above - basically coming up with ways to
turn business problems into data/features you can feed into a model and
picking an appropriate model. How much code you write and the tools/languages
you are using will depend on the job and size of the company.

