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I have several questions regarding this:

1) Do FAANG companies hire non-PhDs for machine learning positions? Most seem to require a MS or PhD

2) What are the interview questions like at FAANG companies for machine learning positions? Is the interview different if you don't have a PhD?

3) For non-PhDs applying, what are the math requirements for the job?

4) For people that have a PhD working in ML at a FAANG, do you feel like you use your PhD level skills day-to-day?




> 1) Do FAANG companies hire non-PhDs for machine learning positions? Most seem to require a MS or PhD

That depends on what you mean by "machine learning positions" and what your bar for normalcy is. For research roles - these generally have distinct titles like "Research Scientist" or "Quantitative Researcher" - it is extremely difficult to get an interview without a PhD, let alone an offer. It happens, but rarely, because there are many capable people with PhDs and other relevant experience applying for the same roles.

If instead you relax the bar to also include software engineers who work with research scientists on model implementation and optimization then yes, people with "only" an MSc are routinely hired for these positions. These engineers are still credited on papers published as a result of their collaboration, and they still need to have a firm understanding of how the models work. The difference is that they don't tend to have leadership roles and don't develop novel theory - they are responsible for supporting the core research team and helping the research output become production ready software.

Both of these types of roles require strong coding skills, but the "hard" research roles require significantly stronger mastery of linear algebra and probability theory. As an example, compare the roles for Research Scientist[1] and Research Engineer[2] at Facebook. You'll find a similar bifurcation at other industry labs like Google, Microsoft and IBM.

If you have the opportunity to do either and you're optimizing your career for wealth maximization or research impact, it's better to obtain a role as a research scientist. That being said most PhDs do not end up at Google Brain or FAIR, so it's not a cut and dry decision. You can't just choose to trade n years of your life and an easier-to-obtain role on the periphery of research for the ability to do theoretical research at the best tech companies in the world later on.

________________________________

1. https://www.facebook.com/careers/jobs/a0I1H00000Mp2ZCUAZ/

2. https://www.facebook.com/careers/jobs/a0I1H00000LJm3MUAT/


Thanks. Can you list what courses one should focus on as a baseline to do research in ML? You mentioned Probability theory & Linear algebra, but what specific topics in those areas should one learn? Any other fields of math?


Probability, statistics and linear algebra are recommended because all of machine learning uses these as foundations. Any graduate level course in these areas will cover what you could possibly need. For people working in the more theoretical side of machine learning, maybe some analysis (functional specifically) might be useful.


I work at NVidia as a GPU architect doing ML applied research. I'm not a hiring manager, but I have interviewed a bunch of people.

1) Having a PhD will make it easier to get an interview, but it is not necessary. Relevant experience counts just as much. I only have an MSc.

2) That will vary a ton from one team to another and from one position to another. The interviewers are not going to change their questions depending on your education.

3) The same requirement as for PhDs. It will depend on the role, but in general I expect people value hands-on experience more than theory.

Except for some particular hiring managers with strong opinions, a PhD is not going to be a hard requirement for nearly any job. However, a PhD in a relevant area is going to be useful, just like any other relevant experience you may have.


The best machine learning researcher I know has a MsC in EE. This is at a research lab that begins with an M.


MIT Media Lab, obviously.


I'd like to know a variation on this:

> 4) For people that have a PhD working in ML at a FAANG, do you feel like you use your PhD level skills day-to-day?

5) For people who work with a mix of PhDs and non-PhDs in the same field, do you notice a difference in output quality?


5) For people who work with a mix of PhDs and non-PhDs in the same field, do you notice a difference in output quality?

No, individual differences outweigh any pattern that I've seen.


5) No, but I am obviously biased due to not having a PhD. I doubt you are going to get an unbiased answer from anybody; everybody wants to believe they made the right choice.


> I doubt you are going to get an unbiased answer from anybody; everybody wants to believe they made the right choice.

I'm not sure where choices come in here. I specifically mentioned a mixed team; the choices have already been made, so the performance of the existing team is what I'm asking about.

As far as not getting an unbiased answer, that's why I'm asking HN at large -- hopefully there are enough people in enough environments to give an interesting and informative combination of answers. :)




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