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.
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1. https://www.facebook.com/careers/jobs/a0I1H00000Mp2ZCUAZ/
2. https://www.facebook.com/careers/jobs/a0I1H00000LJm3MUAT/
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.
> 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?
No, individual differences outweigh any pattern that I've seen.
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. :)
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?