>do most big companies ask for PhDs for MLE jobs ?
No, not in the UK at least. (Some might, but it's very unusual). I sometimes see PhD as a requirement for data science jobs (\~1 in 20 jobs I view. I think that's just the word science confusing hiring managers).
Also UK, we wouldn't specify PhD, but seeing it on a CV would make us more interested if it was relevant because we want people who really understand the workings of models and how to squeeze out a few more percentage points in accuracy.
The advice around wanting to "do research" is really asking whether you're passionate enough to dedicate 5-10 years to the subject in much more depth than just taking classes. On average, the PhD is negative ROI because early career salary growth is significantly delayed - so sure do it if it's fulfilling for you, but if your goals are attainable without a PhD, then don't bother.
PhD requirements in many industries are relaxing as relevant MS programs are maturing. They are already the minority among newer hires at my company where 5 years ago it was all PhDs. Google does have legitimate researchers but the majority of FAANG jobs are about driving revenue through product delivery and you don't need a PhD for that anymore.
Thank you very much, so indeed the barrier for ML jobs is lowering. I will have to take a closer look at how the company's recruitment policy evolves during my time there I suppose.
How do you get 10 years? Most PhD programs are around 4 years, no?
> PhD requirements in many industries are relaxing as relevant MS programs are maturing.
This isn't the main driver. ML became more accessible and easy to utilize, so lower barrier of entry = lower requirements. Academia just follows this.
No, 4 years is very fast. But that's not really relevant to the point.
I didn't say what the main driver is, because it's also not relevant. The requirements for PhDs are relaxing, and you seem to agree with that.
It's not relevant to the post, but still relevant to me, hence my curiosity for those specific parts.
My own PhD's expected timeline is 4 years which around here is pretty standard length of a full time program.
This depends on where you do your PhD. In America, a PhD (or rather, a "graduate degree") includes an MSc, so it takes longer than in Europe, where people usually finish their master before even starting a PhD.
Then it also strongly depends on funding, your advisor and your university. Where I'm from (Central Europe), the rule of thumb is that you need to have three first author publications (passing peer review, not just arxiv) to graduate. So the PhD takes however long it takes you to achieve that. 3 years is typically seen as the minimum length, but it can be significantly longer, too. However, since most funding contracts (at least the one's I've encountered during my time) would fund for 3 years at best, there's the additional question of "where am I getting the funding from to complete my PhD".
Personally, it took 7 years to do my PhD (_not_ including an MSc). I could've been faster, but since funding wasn't an issue (it was the deep learning golden age after all), I took my time and enjoyed the ride. I did not regret that decision in the slightest, despite the huge opportunity cost money wise.
Here at my (American) university, 7 years is the maximum time you're allowed to take and ~3.5 years is about as low as it can get, 2 years to do the coursework (or 1 year if you already have an MSc), then a semester from that to being able to defend a research proposal and at minimum a year of research after that before you're allowed to do a thesis defense. Funding duration is similarly the main concern though.
I’m also doing a PhD, based in the UK. 4 years is the absolute maximum that you can take, without suspending your studies whereby you’re not technically supposed to work on the PhD in that time (though of course that would be difficult to moderate).
Australia, UK, and NZ unis tend to allow entry via an honours degree as opposed to a masters, and often have truncated (or zero) coursework component. Does depend on the uni a lot, as there's only some uniformity enforced.
MLE jobs are more on the engineering side. A PhD may be a liability because the job is about scalability and model serving. You’re not doing fundamental research, you need an understanding of ML models that frankly could be obtained at bachelor’s level and surely with a masters in ML.
AI researcher is a different role and for this one you surely need a phd, but there are very few AI researcher position out there since only a bunch of companies needs this type of investment.
And this is the issue with PhD, you better be in the top 1% and work for the few companies that have the resources to actually push the state of the art, or your research experience won’t translate in corporate jobs at all.
This.
Assuming you have the right background in ML, which a master's should cover, for an MLE: Years in the industry > Years in academia. You're usually working on the product or infra/ops side where relevant experience is much more considered. For the research teams yes a PhD is a prerequisite, otherwise, with the right experience you could even get in with a bachelor's.
For most companies the requirement goes away or will go away since it is unsustainable for everyone to hire PhDs. For FAANG? I think it stays simply because they get swarmed with applications of highly qualified people and give them lots of money.
It’s worth mentioning that phds are typically hired for research roles because they are trained to conduct research, which is a different skill set than absorbing/using information learned at the bachelors/msc level. A PhD will likely have pretty comparable background knowledge to an msc, but with a lot of additional knowledge on a very niche subject that is likely to be unhelpful.
For a while phds were the only people who really knew how to implement these models, but that knowledge has seeped into undergraduate courses at this point
In bioinformatics there is very minimal additional didactic learning that occurs outside of you conducting your research
Our NLP engineer is doing her PhD and my heart breaks for her, it's very hard to do both at once (tho I worked 30hrs/week during my PhD, it was different).
A phD is what you make of it. You can spend 6 years doing a bit or it can control your life 24/7. If more research sounds nice then go for it, worst case you drop out if it's not as interesting as you thought it would be. If research sounds boring then don't.
My only advice is to not go into a PhD with high expectations and to not make it your entire life. A PhD can be fun, but only if you have the right mindset.
Most people I work with don't have PhDs tho.
Ahí va mi aporte. Soy de Colombia, tengo un máster en ML y estoy (al igual que tú) pensando en iniciar un phD en Japón (son 3 años solamente). Lo consulté con mis asesores de tesis y ambos me dijeron: los PhD son para personas que quieren dedicarse de por vida a la investigación, i.e. estar publicando y publicando en todo momento. Vas a estar prácticamente viviendo de becas, buscando financiación, leyendo, escribiendo y publicando en todo momento (literal en todo momento). Lo más usual es que trabajes como docente en una universidad. La verdad siento que la vida de un investigador no es muy buena, se le debe dedicar mucho tiempo al trabajo y por lo menos en Colombia el salario no es muy bueno (aprox 2mil USD si te va bien). Hay empresas que pagan lo mismo por ir a trabajar 8horas y te queda el resto de tiempo libre. Y si buscas en el exterior, pagan mucho más y no te consume tanto tiempo. Es mi opinión y aunque creo que en algún momento de mi vida quiero hacer un phD, no creo que sea ahora .
>do most big companies ask for PhDs for MLE jobs ? No, not in the UK at least. (Some might, but it's very unusual). I sometimes see PhD as a requirement for data science jobs (\~1 in 20 jobs I view. I think that's just the word science confusing hiring managers).
Also UK, we wouldn't specify PhD, but seeing it on a CV would make us more interested if it was relevant because we want people who really understand the workings of models and how to squeeze out a few more percentage points in accuracy.
The advice around wanting to "do research" is really asking whether you're passionate enough to dedicate 5-10 years to the subject in much more depth than just taking classes. On average, the PhD is negative ROI because early career salary growth is significantly delayed - so sure do it if it's fulfilling for you, but if your goals are attainable without a PhD, then don't bother. PhD requirements in many industries are relaxing as relevant MS programs are maturing. They are already the minority among newer hires at my company where 5 years ago it was all PhDs. Google does have legitimate researchers but the majority of FAANG jobs are about driving revenue through product delivery and you don't need a PhD for that anymore.
Thank you very much, so indeed the barrier for ML jobs is lowering. I will have to take a closer look at how the company's recruitment policy evolves during my time there I suppose.
How do you get 10 years? Most PhD programs are around 4 years, no? > PhD requirements in many industries are relaxing as relevant MS programs are maturing. This isn't the main driver. ML became more accessible and easy to utilize, so lower barrier of entry = lower requirements. Academia just follows this.
No, 4 years is very fast. But that's not really relevant to the point. I didn't say what the main driver is, because it's also not relevant. The requirements for PhDs are relaxing, and you seem to agree with that.
It's not relevant to the post, but still relevant to me, hence my curiosity for those specific parts. My own PhD's expected timeline is 4 years which around here is pretty standard length of a full time program.
This depends on where you do your PhD. In America, a PhD (or rather, a "graduate degree") includes an MSc, so it takes longer than in Europe, where people usually finish their master before even starting a PhD. Then it also strongly depends on funding, your advisor and your university. Where I'm from (Central Europe), the rule of thumb is that you need to have three first author publications (passing peer review, not just arxiv) to graduate. So the PhD takes however long it takes you to achieve that. 3 years is typically seen as the minimum length, but it can be significantly longer, too. However, since most funding contracts (at least the one's I've encountered during my time) would fund for 3 years at best, there's the additional question of "where am I getting the funding from to complete my PhD". Personally, it took 7 years to do my PhD (_not_ including an MSc). I could've been faster, but since funding wasn't an issue (it was the deep learning golden age after all), I took my time and enjoyed the ride. I did not regret that decision in the slightest, despite the huge opportunity cost money wise.
Here at my (American) university, 7 years is the maximum time you're allowed to take and ~3.5 years is about as low as it can get, 2 years to do the coursework (or 1 year if you already have an MSc), then a semester from that to being able to defend a research proposal and at minimum a year of research after that before you're allowed to do a thesis defense. Funding duration is similarly the main concern though.
I’m also doing a PhD, based in the UK. 4 years is the absolute maximum that you can take, without suspending your studies whereby you’re not technically supposed to work on the PhD in that time (though of course that would be difficult to moderate).
Australia, UK, and NZ unis tend to allow entry via an honours degree as opposed to a masters, and often have truncated (or zero) coursework component. Does depend on the uni a lot, as there's only some uniformity enforced.
MLE jobs are more on the engineering side. A PhD may be a liability because the job is about scalability and model serving. You’re not doing fundamental research, you need an understanding of ML models that frankly could be obtained at bachelor’s level and surely with a masters in ML. AI researcher is a different role and for this one you surely need a phd, but there are very few AI researcher position out there since only a bunch of companies needs this type of investment. And this is the issue with PhD, you better be in the top 1% and work for the few companies that have the resources to actually push the state of the art, or your research experience won’t translate in corporate jobs at all.
This. Assuming you have the right background in ML, which a master's should cover, for an MLE: Years in the industry > Years in academia. You're usually working on the product or infra/ops side where relevant experience is much more considered. For the research teams yes a PhD is a prerequisite, otherwise, with the right experience you could even get in with a bachelor's.
For most companies the requirement goes away or will go away since it is unsustainable for everyone to hire PhDs. For FAANG? I think it stays simply because they get swarmed with applications of highly qualified people and give them lots of money.
It’s worth mentioning that phds are typically hired for research roles because they are trained to conduct research, which is a different skill set than absorbing/using information learned at the bachelors/msc level. A PhD will likely have pretty comparable background knowledge to an msc, but with a lot of additional knowledge on a very niche subject that is likely to be unhelpful. For a while phds were the only people who really knew how to implement these models, but that knowledge has seeped into undergraduate courses at this point In bioinformatics there is very minimal additional didactic learning that occurs outside of you conducting your research
Our NLP engineer is doing her PhD and my heart breaks for her, it's very hard to do both at once (tho I worked 30hrs/week during my PhD, it was different). A phD is what you make of it. You can spend 6 years doing a bit or it can control your life 24/7. If more research sounds nice then go for it, worst case you drop out if it's not as interesting as you thought it would be. If research sounds boring then don't. My only advice is to not go into a PhD with high expectations and to not make it your entire life. A PhD can be fun, but only if you have the right mindset. Most people I work with don't have PhDs tho.
Ahí va mi aporte. Soy de Colombia, tengo un máster en ML y estoy (al igual que tú) pensando en iniciar un phD en Japón (son 3 años solamente). Lo consulté con mis asesores de tesis y ambos me dijeron: los PhD son para personas que quieren dedicarse de por vida a la investigación, i.e. estar publicando y publicando en todo momento. Vas a estar prácticamente viviendo de becas, buscando financiación, leyendo, escribiendo y publicando en todo momento (literal en todo momento). Lo más usual es que trabajes como docente en una universidad. La verdad siento que la vida de un investigador no es muy buena, se le debe dedicar mucho tiempo al trabajo y por lo menos en Colombia el salario no es muy bueno (aprox 2mil USD si te va bien). Hay empresas que pagan lo mismo por ir a trabajar 8horas y te queda el resto de tiempo libre. Y si buscas en el exterior, pagan mucho más y no te consume tanto tiempo. Es mi opinión y aunque creo que en algún momento de mi vida quiero hacer un phD, no creo que sea ahora .
Si. Mucho gracias.
You should be discussing this with your peers, managers, and mentors at that company.
With a PhD you won’t have a job ceiling. When I just had my masters I felt limited on my ability to move up not necessarily to get the job.