[HN Gopher] The Machine Learning Job Market
___________________________________________________________________
The Machine Learning Job Market
Author : sebg
Score : 192 points
Date : 2022-04-25 14:58 UTC (8 hours ago)
(HTM) web link (evjang.com)
(TXT) w3m dump (evjang.com)
| awinter-py wrote:
| > The most important deciding factor for me was whether the
| company has some kind of technological edge years ahead of its
| competitors. A friend on Google's logging team tells me he's not
| interested in smaller companies because they are so
| technologically far behind Google's planetary-scale infra that
| they haven't even begun to fathom the problems that Google is
| solving now, much less finish solving the problems that Google
| already worked on a decade ago.
|
| ^ this is increasingly a choice in your career -- 'where can you
| go to solve big problems', and 'big problems are increasingly
| complex'
|
| scale is real, and tools matter. you can spend your whole project
| burn at the wrong company building something that you could buy
| somewhere else, or which already exists at a competitor
|
| slight grain of salt here is that G's logging system, from my
| perspective as a gcp user, is slow as balls and the UX is the
| incarnation of scroll jank. and also this (very good) article led
| to the outcome of the author building soft hands happy-ending
| robots
| [deleted]
| hintymad wrote:
| I think this reasoning is flawed. Joining Google does not mean
| you get to solve interesting problems because you will most
| likely contribute incrementally to a vast body of work. You
| want to build the next-gen database? You have to come up with
| something way better than BigTable and Spanner. You want to
| build a queue service? You've got to come up with something way
| better than Google's PubSub, which optimizes itself all the way
| down to the level of GBP protocols. You want to build a machine
| learning framework? Have you checked out TensorFlow and JAX in
| particular their ecosystem? You want to build a file system?
| Are you sure you can overcome all the organizational inertia
| that Google has built on Colossus over the years? You want to
| build a 10X more productive framework for data processing? Are
| you sure you can beat DataFlow or Flume4J-ecosystem in Google?
|
| The point is, Google is a mature company. Mortals like most of
| us don't get to break ground in a technically advanced but
| mature company like Google. Instead, we find fast growing _new
| problems_ to solve, to hone our skill, and to get to scale.
|
| P.S., I personally know a number of prominent professors used
| to work on the Borg projects just to optimize for a few percent
| of gains. It's deep and interesting work, but nonetheless hard
| for mortals like me to get much out of.
|
| That is, it's a more sure bet to work for a baby Google than to
| work for a middle-aged Google.
| anonymousDan wrote:
| Yes or work on things that are fundamentally at odds with
| Google's business model, or on systems that in some way are
| difficult for Google to do because they have company-spe ific
| constraints or legacy systems
| oneoff786 wrote:
| On the other hand, there's a lot of real problems that real
| people actually deal with that just need a logistic regression
| to save million bucks here and there. I like that space more.
| axg11 wrote:
| This describes 95% of machine learning at FAANG+,
| unfortunately nobody likes to talk about. Context: I work at
| a FAANG.
| difflens wrote:
| If you have a few minutes, can you list a few of these
| problems? Just curious here!
| geebee wrote:
| I've heard about this, in different contexts. What it mainly
| comes down to is that incremental improvements can have
| massive impacts when you can apply them at a scale available
| at FAANG. I first read about this outside the context of
| machine learning, but it certainly would apply here.
|
| For those of us who don't work at such scale, can you (maybe
| with a little fuzziness to avoid telling too much about an
| internal project) give a few examples of the kind of projects
| where a fairly simple model can have a 1M+ impact?
| nerdponx wrote:
| This space isn't sexy to write about, there's no fame and
| glory in it.
| tomrod wrote:
| Far sexier and larger than most care to admit.
| Terry_Roll wrote:
| The trappings are...
| https://www.youtube.com/watch?v=4VapnDFoR2U
| [deleted]
| htrp wrote:
| >G's logging system, from my perspective as a gcp user, is slow
| as balls and the UX is the incarnation of scroll jank
|
| does GCP use G's internal tools?
|
| Inquiring minds would like to know
| adam_arthur wrote:
| Not every problem is one of scale. And with the advent of
| serverless, problems relating to scalability will be largely
| abstracted from 99% of developers in the future and more of a
| niche knowledge domain. Just as the inner workings of the OS
| are largely not well understood by most developers
|
| Obviously the principles and theory behind scalability is still
| important for properly structuring your app, but there won't be
| many novel problems to solve, and increasingly obvious
| architecture choices as time goes on
| rychco wrote:
| This has become increasingly important to me too. I am employed
| by a small (2-4 engineers at any time) company and I'm often
| disappointed because we're just _so_ far behind in manpower &
| technical expertise that we have to dramatically reduce the
| scope of any problem we want to tackle.
|
| On the other hand, I also worry about getting sucked into the
| bureaucracy of FAANG sized companies & not having any
| accountability or agency over what I work on. (I realize this
| is a sweeping generalization of FAANG, but some of my peers
| have had this experience even a few years into their jobs)
| fxtentacle wrote:
| I'm surprised. Apart from DALLE, I haven't seen any AI
| approach that's off limits for 4 highly motivated people with
| 3090 GPUs.
|
| At that compute level, you should be able to at least
| replicate SOTA in optical flow, structure from motion, speech
| recognition, text to speech, translation, text summary,
| sentiment analysis, image classifications, image
| segmentation, and of course playing video games or optimizing
| processes with reinforcement learning.
|
| I mean thanks to KiCad even custom sensor hardware is cheap
| these days.
|
| Can you give more details about what you tried to do and why
| that wasn't possible?
| altdataseller wrote:
| You are talking about 2 commodities that can easily be
| upgraded: logging and UX. Hell, I wouldn't even waste too many
| precious resources on improving those things past a point.
|
| Google has massive data and scaling advantages that can never
| be duplicated or fixed by smaller companies.
| YossarianFrPrez wrote:
| Say what you will about the OP and the claims he makes in his
| career announcement... I was struck by how casually the author
| mentioned that Academia is behind various private companies. Call
| me romantic, for in an ideal world Academia is just as close to
| the cutting edge of knowledge as private R&D laboratories.
| water-your-self wrote:
| >In the future, every successful tech company will use their data
| moats to build some variant of an Artificial General
| Intelligence.
|
| Its rare that an article loses my faith in the first sentence.
| angarg12 wrote:
| I don't want to derail the conversation, but OPs career path
| really stood out to me.
|
| He graduated in 2016, worked at Google in Bay Area, and now is
| joining a startup at a VP level.
|
| I graduated in 2008, obtained a PhD in 2014 in a no name EU
| university, worked in odd companies for a while and joined FAANG
| 4 years ago as a mid level developer, where I am still ATM.
|
| Looking at this disparity I wonder what could be possible
| explanations:
|
| * OP is a beast and has grown very quickly in a short time.
|
| * I'm particularly inept and I'm growing very slowly.
|
| * Working in the right conditions (e.g. Bay Area, Big Tech, right
| team) can greatly accelerate your growth.
|
| * Startups have a big title inflation.
| throw1234651234 wrote:
| If it makes you feel any worse, a fresh-college grad just
| worked his first month at any job on my team at a midwest
| company and got an offer at Google making more than I am now. I
| have over a decade of experience and endless Cloud Architect
| certs (all 3 clouds) as well as a background in finance.
|
| Right place, right time + talent + willingness to take risk.
| filoleg wrote:
| Without knowing the deatils, it is mostly #4, with a good doze
| of #3, and potentially a decent amount of #1.
|
| Basically, yeah, small/not-yet-massive startups have insane
| overinflation in titles. Had plenty of former college
| classmates who became "VPs" or "staff engineers" at super small
| startups a couple years out of college. Getting plenty of
| recruiter messages on linkedin myself for "staff engineer"
| positions at random startups, despite me not even being a
| senior at a FAANG yet, and only being about 4.5 years out of
| college.
|
| Another thing is, no matter how smart or hard working you are,
| being in the right place at the right time is extremely
| important. It won't help much if you lack skills, but being in
| the right place at the right time is like a force multiplier on
| your skills and the work you do. Which is partially why most of
| the big opportunities are still heavily concentrated in a few
| geographic spots (despite there being no tangible technical
| need for that).
|
| Don't beat yourself up over it, titles don't mean that much.
| You are able to start a one-man-shop LLC and call yourself a
| VP, a director, or whatever else you want. The real question
| is, with that title, are they being compensated as much as you
| are? If they decide to quit and get a job at a "regular" tech
| company after, will that VP title translate into anything more
| than an L4/L5? Just some food for thought.
| twomoonsbysurf wrote:
| I know a former Amazon Engineer. After working at Amazon as a
| mid level engineer, co-founded his own startup in Mexico, as
| CTO.
|
| It's a startup... titles in a 50 people organization don't
| compare to 50,000 people organization titles.
|
| I'm sure you can go and be a VP at a startup too, if that's
| what you want to do. Just go and network at Incubator,
| Investor, & Entrepreneur events/meetups/organizations, and come
| up with an idea & customers, then execute and try to get
| customers on board... rinse and repeat.
| axg11 wrote:
| Don't despair. I work for a FAANG and have previously worked at
| startups. Title inflation at startups is a huge factor. In
| fact, titles are not equivalent between any two companies. I
| have seen startup CTOs (even series A) transition to senior
| engineer IC roles at a FAANG.
|
| As you identified, location is the next big factor. If you are
| still in Europe, my advice is to leave or to start your own
| company there. If you are working for primarily US based
| companies in Europe there will always be a limit to the level
| of exposure you get to leadership and to how fast you can rise
| up the hierarchy.
|
| Finally, don't discount Eric's profile. Through some
| combination of his public profile and professional work, he's
| established a reputation and following. That is just as
| important as any hard engineering work in securing a
| senior/leadership role.
| going_ham wrote:
| Yeah, and the way the author presents themselves speaks volume
| too. There is a real pride in this essay. You can see how the
| author casually drops big names and insights like it is a fact.
|
| Why does valley culture makes it seem like everything is
| possible and anything innovative can happen soon? The
| innovation in AI really seems like it is being made on a thin
| line of engineering and compute. It doesn't happen overnight.
| It requires some people working through and through to pull it
| off. These days it requires collective contribution.
| samhw wrote:
| > The innovation in AI really seems like it is being made on
| a thin line of engineering and compute.
|
| This perfectly echoes my own thoughts. The advances being
| trumpeted in AI are functions of hardware advances that allow
| us to have massively overparameterised models, models which
| essentially 'make the map the size of the territory'[0],
| which is why they only succeed at a narrow class of
| interpolation problems. And even then nothing useful. That's
| why we're still being sold the "computer wins at board game"
| trope of the 90s, and yet somehow also being told that we're
| _right on the verge of AGI_.
|
| (OK, it's not only that. There's also a healthy amount of
| p-hacking, and a 'clever Hans effect' where the developer
| likely-unconsciously intervenes to assure the right answer
| via all the shadowy 'configuration' knobs ('oversampling',
| 'regularisation', 'feedforward', etc). I always say: if you
| develop a real AI, come show me a demo where it answers a
| hard question _whose answer you - all of us - don 't already
| know_.)
|
| [0] Or far larger, actually. Google the 'lottery ticket
| hypothesis'.
| ChefboyOG wrote:
| Eh, if you boil all research in AI/ML down to the binary of
| "AGI or bust," then sure, everything is a failure.
|
| But, if you look at your smartphone, virtually every
| popular application the average person uses--Gmail, Uber,
| Instagram, TikTok, Siri/Google Assistant, Netflix, your
| camera, and more--all owe huge pieces of their
| functionality to ML that's only become feasible in the last
| decade because of the research you're referencing.
| going_ham wrote:
| These are engineering marvel! This is engineering at it's
| finest. Applied math at it's finest. So it's not a
| failure.
|
| The way people hype AGI/AI/ML whatever undervalues the
| actual effort behind these remarkable feat. There is so
| much effort being made to make this work. Deep learning
| works when it is engineered properly. So it is just
| another tool in the toolbox!
|
| Look at how graphics community is approaching deep
| learning. They already had sampling methods but with MLPs
| (NeRFs), they are using it as glorified database. So it's
| engineering!
|
| I want to underscore that AI/ML/DL research requires
| ground breaking innovation not only in algorithms but
| also in hardware and software engineering.
| atorodius wrote:
| > ,make the map the size of the territory'[0], which is why
| they only succeed at a narrow class of interpolation
| problems
|
| I take it you have not seen the recent Dall-E 2 results?
| Clearly that model is not just working on a narrow space.
|
| See https://openai.com/dall-e-2/ and the many awe-inspiring
| examples on Twitter
| visarga wrote:
| I disagree, there are plenty of amazing advancements in the
| last 2 years you can't write off like that (especially
| Instruct GPT-3 and Dall-e 2). For example I have worked on
| a ML project in document information extraction for 4
| years, and recently tried GPT-3 - it solved the task zero
| shot.
| bsenftner wrote:
| > show me a demo where it answers a hard question whose
| answer you - all of us - don't already know.
|
| For that, we need artificial comprehension, which we do
| not. Artificial comprehension, the ability to generalize
| systems to their base components and then virtually operate
| those base concepts to define what is possible, to virtual
| recreate physical working system, virtually improve them,
| and with those improvements being physically realizable is
| probably what will finally create AGI. We need a Calculus
| for pure ideas, not just numbers.
| amelius wrote:
| Machine Learning expert is the new Web Developer, so expect the
| former title to become diluted very quickly.
| ZephyrBlu wrote:
| Probably 1 + 3 + 4. He didn't just work at Google, he worked at
| Google Brain. And with only a Bachelor's, so I assume he's is
| both very smart and got very lucky.
| rakejake wrote:
| Probably a combination of 1, 3 and 4. There are tons of
| talented people even at the top but the stars have to align to
| achieve more than your expected value.
|
| PhD -> low-level dev -> FAANG mid-level is nothing to scoff at
| so you're doing pretty well.
| [deleted]
| rcpt wrote:
| I'm the same deal as you. I don't really have any problems with
| it. I worked at a startup and titles are much fancier but you
| don't get to ship to a billion users. Also the infrastructure
| can suck real bad.
|
| Competing within a giant company for perf ratings feels like
| school and I'm over it. But the other parts of the job are
| great.
| time_to_smile wrote:
| Tech has a bad habit of conflating comp/prestige with skill. I
| have no doubt the OP _is_ quite good at what they do, but you
| not being where OP is does not therefore imply you _don 't_
| have skill.
|
| Unfortunately the tech world is not really a meritocracy.
|
| When I look at my own circle of technical people the most
| incredible ones from a pure technical ability are divided
| between working at FAANG making 500k+ and working a relatively
| unknown companies making ~200K or less. One of the most
| mindbendingly brilliant people I know is working in relative
| obscurity, known very well only among other people that are top
| in the field, but their resume looks very ordinary compared to
| their behind the scenes contributions to major projects.
|
| Managing a career in tech is largely independent from technical
| skills and abilities. I have met a shocking number of people
| making lots of money at prestigious institutions that are "meh"
| as far as technical ability goes (of course there's some great
| ones as well), and have met plenty of brilliant people working
| relative obscurity.
|
| The success is largely a function of both background (Brown
| does beat a "no name EU university") and personal desire to
| have a prestigious career. There is a lot of self promotion
| going on in this piece, in fact the OP has already convinced
| you that they might be just a wildly better person than you. If
| they can convince you they are this amazing, then they also can
| convince the leadership team at a start up. But do recognize
| that their skill demonstrated so far is only in convincing you
| of this.
| sleepdreamy wrote:
| There is more to being a great tech employee than just being
| 'brilliant' at the hard skills. Soft skills are just as
| important, and play a role behind why I have been promoted
| more than peers who surpass my skills ten-fold. Some people
| also don't want to be in management.
|
| We all have different trajectories and choices. This comment
| makes it seem like if you aren't a technical wizard then you
| might as well be useless. This is not reality
| freyr wrote:
| Also, the author grew up in the Bay Area (early exposure to how
| the SV ecosystem works), went to an Ivy league school (opens
| doors to top internships), landed those internships, and got a
| masters. All those things, but especially the internships, can
| help fast track your early career.
|
| I'd take some things here with a grain of salt, like _"low 7
| figures compensation (staff level)"_ at FAAN (can eliminate G
| because they're not likely to hire him back at L+1 immediately
| after he quits). ML is still somewhat hot, but 7 figures is an
| outlier for staff-level comp.
|
| AAPL: ~$450k
| https://www.levels.fyi/company/Apple/salaries/Software-Engin...
|
| AMZN: ~$600k
| https://www.levels.fyi/company/Amazon/salaries/Software-Engi...
|
| FB: ~$600k
| https://www.levels.fyi/company/Facebook/salaries/Software-En...
|
| Nobody there is reporting $1M+ offers for staff level. While
| I'm sure it's happened, it's pretty far outside the staff pay
| band (excluding equity gains during the 2020-2021 market run-
| up, which are sadly behind us) and would be a truly exceptional
| offer even in the current climate. That, plus the fact that it
| sounds like he didn't get many formal offers ("I did not
| initiate the formal HR interview process with most of them")
| and wasn't pitting offers against each other, makes me
| skeptical.
| marcinzm wrote:
| I was a VP at a startup (~200 people) before I was 30 without a
| PhD. It was all BS and I had less manager qualifications than a
| FAANG line manager. I got lucky.
|
| It's clear from the blog post that the author is in the same
| boat. They lament CEOs not having time to do research but took
| a VP position. An actual VP doesn't have time do research so
| they're clearly not an actual VP. So they're likely a tech lead
| with an inflated title.
| Swizec wrote:
| > Working in the right conditions (e.g. Bay Area, Big Tech,
| right team) can greatly accelerate your growth
|
| This is the answer. I have grown more in ~7 years* of random
| SFBA startups than I did in the previous 13 years of career in
| Europe. Just because the kind of startup that's a dime a dozen
| over here is a once in a lifetime opportunity back home.
|
| To put this contrast into numbers: In 2021, during the pandemic
| while "SFBA is dying" was the mem, the Bay Area raised as much
| startup investment _as all of Europe_.
|
| *I wasn't as career aggressive as I could've been, mostly for
| visa-related reasons.
| [deleted]
| yowlingcat wrote:
| A few comments:
|
| 1) The PhD takes a huge hit on your opportunity cost.
|
| 2008-2014 is 6 years of time; for me, it was the delta between
| starting my career as a junior engineer and becoming a tech
| lead at a hot unicorn which let me pivot to a CTO role at a
| small startup.
|
| 2) Academic credentialism has real effects.
|
| This guy did a CS degree at an Ivy in the US. He has been set
| up for commercial success in the US tech industry through a
| halo effect you cannot also access unless you gained access to
| that institutional grooming at the same age. By choosing to do
| that PHD in EU (and a no name one at that), you forfeited that
| access.
|
| In my experience, while the effect of this goes down over time,
| it has extremely strong launch + early compounding effects.
|
| 3) Risk tolerance can work to your benefit or against it.
|
| You are working at a FAANG which is the safest and most cash
| lucrative option. In all likelihood, you have a great WLB and
| now a great blue chip brand on your resume. However, the cost
| of this is that you're generally not going to get access to
| projects or culture that, by virtue of your participation, set
| you on an extremely steep growth path.
|
| To get access to that, IMO, there's no real alternative to
| achieving strong outcomes working at a startup. Of course, that
| can be hard to do -- how do you figure out which ones are
| future winners, and how do you get them to let you come on
| board? I have no great answer rather than early career trial
| and error (accepting some of it will work out poorly and
| uncomfortably so).
|
| I wouldn't say that "OP is a beast" per se, but it's much more
| likely that they have been groomed (working in the right
| conditions) in ways that you may not have. And yes, startups
| titles are not comparable to big company titles. It's apples
| and oranges.
|
| The company he joined is a Series A startup, so absolutely an
| early stage company where whether you're VP/CXO, you're
| functionally going to be doing a player/coach role at most with
| tons of strategy baked in. But I wouldn't call that inflation,
| per se. Sure, it's not the equivalent of being an experienced
| people leader and executive at a big corporation manning a
| giant organization at its helm. But you are often times in
| charge with significantly more responsibility and do not have
| bureaucratic friction and slow pace to hide behind. Doing a
| startup is just different. It's insanely risky, overall has
| poor risk adjusted rewards, and often is a magnet for shady
| characters. But if you can filter out the wheat from the chaff,
| you get access to the best career opportunities available, bar
| none.
| going_ham wrote:
| This post feels like author is insecure about his position and
| wants to establish some validity. Having going through it all, it
| feels delusional at best.
|
| The glorified pattern matching can only take us so far. You know
| it's working as long as there is a pattern. I wouldn't call it a
| general intelligence per se. There is no "juice" in these
| algorithms.
|
| If we use these tools, we can immediately see where they fail and
| where they do not. These are just a new tools in software
| engineer's box.
| visarga wrote:
| > The glorified pattern matching can only take us so far.
|
| This argument is becoming less and less convincing year by the
| year. We're amazed that things we were sure couldn't be done
| are actually done.
| water-your-self wrote:
| This argument has been on going, on and off, since the 1960s.
| Show me a research paper.
| visarga wrote:
| 1960s were 60 years ago, we're talking about AI, a fast
| paced field in the last 15 years.
| going_ham wrote:
| Definitely! There was a time when compilers were part of
| AI research. Now they are just another tool. Same with
| DL, they are amazing tool. We need them and they provide
| value if used correctly.
|
| I just didn't want to call it as "intelligent" and use
| this as basis for defining "intelligence." We can call
| them something else. It's learning to do a specialized
| job as intended and in "intelligent" manner. But it is
| not intelligence. Even a small ant is intelligent than
| our current AI systems though they aren't sophisticated
| and can't perform human task, they are intelligent than
| AI system.
|
| I hope that made sense.
| fxtentacle wrote:
| Pattern matching can solve everything, if given enough storage
| and training data. Memorizing trillions of sentences is
| basically what makes GPT3 amazing.
|
| You're absolutely correct that patten matching AIs won't ever
| be truly intelligent. But then again, many humans also never
| exceed what can be simulated with good pattern matching. And an
| AGI household robot only needs to be as smart as the maid that
| it's replacing.
|
| I'm optimistic that pure pattern matching will get us to usable
| AGI AI.
| joe_the_user wrote:
| _Pattern matching can solve everything, if given enough
| storage and training data._
|
| There's never going to be training data for "how things are
| going to be next year". A lot of large scale systems involve
| emergence [1], patterns which previously were not visible
| suddenly appearing. I think even today's AI can do things
| that a bit beyond pattern matching (learning to learn, etc).
| But pure matching as such is inherently limited.
|
| [1] https://en.wikipedia.org/wiki/Emergence
| queuebert wrote:
| That's some godawful typography for such a smart guy.
| [deleted]
| Havoc wrote:
| Interesting that Tesla gets its own row in the pro/con table
| while the faangs get lumped together.
| gcheong wrote:
| If Musk is to be believed their Teslabot is going to be in a
| class by itself.
| bigbillheck wrote:
| If Musk is to be believed, Flint would have clean water.
| nrmitchi wrote:
| Why is this interesting? Tesla is... not a FAANG company?
|
| Not only is there no 'T' in FAANG, but the industry/product is
| _completely_ different.
|
| Or maybe the author just wanted to make a joke about coffee.
| Who knows.
| ackbar03 wrote:
| There's no t in faang
| thunkshift1 wrote:
| This post reeks of someone who doesnt 'need' a job and has made
| enough money already. Good for op to go after 'exciting problems'
| rather than the mundane will I make rent and fees
| lysecret wrote:
| Not sure why this gets so much hate. I think he's a bit too
| optimistic about AGI prospects. But in his position these are all
| interesting and reasonable options.
|
| I'm a bit sceptical on this 10,5,1 whatever year ahead metric he
| pulled from wherever.
|
| Interesting read regardless. My opinion about the next few years
| is that most value will come from finding your niche, creating
| Datasets, iterating and building your ML model (a bit like he
| wrote but without this AGI...)
| minimaxir wrote:
| As a relatively unremarkable data scientist/machine learning
| engineer of about 5 years, I've been keeping an eye on DS/ML
| positions as they tend to give a sense on what is important to
| companies in that space, although I'm not actively looking for a
| new role. More and more positions seem to require Ph.D.
| credentials even for non-senior roles, even though modern DS/ML
| tooling doesn't require it.
|
| If I ever left my job I might have to quit DS/ML and do something
| else entirely.
| antupis wrote:
| DE/ML ops/ Software engineer(data) is many ways new DS. Lots of
| greenfield projects and less competition.
| cwp wrote:
| Nah, you wouldn't have to quit. If you've got 5 years
| experience, even on non-cutting edge projects, the PhD won't
| matter. Sure, you won't be able to get any job you want, but
| there are lots of ML jobs that list a PhD requirement that will
| nevertheless jump at the chance to hire someone with practical
| experience.
| minimaxir wrote:
| Hiring managers are more generous, but HR screening the
| resumes aren't.
| cwp wrote:
| That'll only be a problem if you're up against significant
| number of candidates that have both experience and a PhD.
| HR will certainly filter resumes based on something as
| clear-cut as a degree when they can. But they can't just
| filter everything out and tell the hiring manager he's SOL.
| So you don't have to check all the boxes as long as you
| check enough of them, and you're competitive with the other
| candidates.
|
| At the same time, nobody is going to do all that well if
| they just apply online and cross their fingers. You need
| some kind of human contact, either though an introduction
| to an insider through your network, or through a recruiter
| of some kind. It takes a bit of time to develop the
| relationships, but it's quite doable and worth it, even for
| introverts. Best to start before you're interested in
| changing jobs.
| Kalanos wrote:
| sounds like you are coming up with excuses not to start a company
| SCUSKU wrote:
| I was expecting something along the lines of how AI/ML was
| considered a sexy career path, but there are very few jobs
| available and high competition for those jobs. So, as a result,
| you end up with the only available jobs as data engineering/ML
| ops/backend that supports ML teams. I am happy for this author
| and their success, but they clearly are not representative of the
| majority of the people in the ML job market.
| PartiallyTyped wrote:
| I think I have a decent CV, with quite a bit of experience for
| a master's student. I have been searching for a job in MLE, for
| a bit now with very little to show for it, as I am either
| getting no responses, or responses claiming that they are
| looking for more experienced people, and particularly those
| that had experience with a particular stack.
|
| In all honesty, after 6 years of studying, with 4 of those
| years studying ML, to be told that I lack experience with some
| particular stack as the ~~excuse~~ reason for rejection feels
| like a slap in the face.
|
| And all that ignoring everything that expects 3+ years
| experience for entry level positions.
| dunefox wrote:
| I have been in a similar position with NLP - either you
| already have a lot of experience (maybe even a PhD) or you're
| a mathematician or statistician. Otherwise you're left out.
| PartiallyTyped wrote:
| Funny enough, my experience is NLP and DeepRL.
| dunefox wrote:
| Your best chance might be to apply at small companies.
| They often have data and want to take advantage of it but
| haven't so far. Of course they're not doing any research
| or are Google, Amazon, etc. but hey.
| [deleted]
| mola wrote:
| I suspect they are looking for experienced ppl because they
| don't have one and don't exactly know how to manage ML and
| what to do with it and hope someone else will come and show
| them.
| benibela wrote:
| That is why I am doing a Post Doc
| htrp wrote:
| You have to be doing something wrong or applying for the
| wrong types of jobs (lots of MLE jobs are just glorified dba
| jobs at certain job companies).
| [deleted]
| mellavora wrote:
| where are you located?
| PartiallyTyped wrote:
| Europe. I am open to relocating pretty much anywhere in
| Western Europe or Scandinavia so my horizons are pretty
| open.
| captaincaveman wrote:
| So central or eastern Europe ... yeah, thats gonna be
| tough!
| rdedev wrote:
| Reading comments like this is a bummer. I am currently doing
| my masters with a focus on NLP. At this point I'll be pretty
| happy to get a job even if it just boils down to only
| deploying ML models. Even for such a role of companies expect
| years of experience or a PhD I don't know how I can even get
| a job in the first place
| visarga wrote:
| I sometimes interview candidates for ML engineering roles,
| and let me tell you most of them have trouble with basic
| concepts. It's great when I find someone fluent, for a
| change.
| PartiallyTyped wrote:
| Basic concepts referring to what precisely?
| orzig wrote:
| I can only speak for Boston area, but I've been on teams who
| regularly welcome those with only 1-2 years of experience
| (even if it wasn't that relevant, as long as they had
| relevant schooling). I don't know if you consider that
| positive or negative evidence, but there it is.
| nbardy wrote:
| Start building projects on your own with the most common
| tooling. Having something to show cuts down barriers.
| georgeburdell wrote:
| That was my pre-conception as well. I know some people who have
| tried to switch from other engineering to ML/AI, some taking as
| much as a year off work, and the only successful ones already
| had a network in the Bay Area from their previous associations
| (prestigious university/employer).
| rakejake wrote:
| Yep, at that level, there is a ton of competition. The self-
| driving industry is almost like the video game industry and
| actually has a ton of employees who previously worked in
| video games. They are definitely much better compensated in
| ADAS but the work culture is similar (anecdotally so YMMV).
|
| ML is the sexy wing of the tech industry, so it tends to
| attract the people who are willing to put in the hours (for
| interviews as well as towards work).
| bjornlouser wrote:
| AGI, AGI, AGI, AGI, AGI, AGI...
|
| https://images.squarespace-cdn.com/content/v1/5de799b06bb59b...
| [deleted]
| queuebert wrote:
| "Product impact is even slower than robotics due to regulatory
| capture by hospitals and insurance companies."
|
| The author apparently does not understand regulatory capture and
| is throwing around catch phrases to sound smart. Regulatory
| capture would imply that healthcare encounters less regulation
| than it should due to influence over the relevant government
| agencies. This should increase product impact and reduce time to
| market, the opposite of what he suggests.
| axg11 wrote:
| Doesn't the author's sentence mean: hospitals and insurance
| companies have coopted regulators for the benefits of their own
| businesses, at the detriment of medical device companies
| (developing AI)?
|
| I think the author's point still stands.
| queuebert wrote:
| Possibly that was the intended meaning, but the point is
| still invalid. Hospitals want more cool gadgets. They want to
| be able to treat more conditions and charge more for it. If
| anything, the FDA is a constant annoyance to a healthcare
| provider because it hamstrings them from providing care. This
| is why so many patients are enrolled in clinical trials, to
| get care ahead of the FDA approval time frame.
| redsh wrote:
| See you in Rome
| falsenine wrote:
| As someone whose intention is to go to Medical School and pick up
| programming (+ math skills) to potentially work at the
| intersection of ML + Healthcare, the knowledge of the regulatory
| hurdles expressed is discouraging. Not sure if it really is worth
| the effort to study tech on top of medicine. Are there any people
| with experience within ML + Healthcare/Medicine or know of
| startups that are making great strides within this realm?
| rakejake wrote:
| I used to work in the healthcare vertical. While there are
| regulatory hurdles, they are there for good reason. Move fast
| and break things does not work in this industry and will get
| you fired.
|
| You will have better luck working with one of the larger
| companies who have a good history with the FDA, and more
| importantly, have good relationships with hospitals and
| physicians. They are aware of the time and resources it takes
| to push something out. Pay will not be FAANG level or anywhere
| close, but they usually have great work culture and WLB.
| warner25 wrote:
| Can someone with only 6 years of experience make credible
| predictions about things 20 years in the future?
|
| I'm around 15 years of experience, and my appreciation for my own
| lack of knowledge and ability to make predictions still grows
| with every year.
| xpe wrote:
| I'll bite on the above loaded question: Define experience.
| However you do, it should at least include work, education, and
| life in general, given that such experiences relate to the
| context or situation.
| warner25 wrote:
| Yeah, it was a bit snarky, but still an honest question. In
| some domains, like astronomy or geology or climate change, I
| guess it seems reasonable to make predictions 100 or 1,000
| years in the future based on available data rather than
| experience. In other domains, like politics or economics or
| finance, it seems like there would be much more value in
| having worked through a bunch of election and business cycles
| over the years. I'm not sure where computing and AI sits on
| that spectrum. I can imagine a young AI researcher failing to
| realize that some approach was tried and found to be a dead-
| end 30 years ago.
|
| On the definition of experience, I agree that education and
| life experience counts. I said "around 15" years for myself
| because the definition is fuzzy. I got some very specific
| career preparation and training in college, so that sort of
| counts, and I probably spend more personal time than many of
| my peers learning about relevant history and current events.
| [deleted]
| benibela wrote:
| Or you can go into academia to work on the really hard problems
| on a 5 figures salary.
| spupe wrote:
| A lot of opinions and unverifiable statements (this and this
| company is X years ahead of everyone), and the whole piece is
| essentially about one person's job market. Skip
| melling wrote:
| I'm interested in understanding the ML job market for
| traditional software developers.
|
| Does the opportunity exist to transition into any particular ML
| roles then grow from there?
| claytonjy wrote:
| I've been seeing a lot of Data Engineering/Platform roles
| that support ML without requiring past ML experience. How
| much future lateral movement would be available to you will
| vary widely, but this would be a fairly easy inroad.
| visarga wrote:
| You need 3-4 years of intense study to transition from
| software engineering to ML. It's different enough.
| joshvm wrote:
| Sure, there are many sides to ML. One is the data science
| bit, curating data, picking a good model. Adjacent to this is
| research into new models or training methods.
|
| The other side is deploying it efficiently, and that becomes
| a more routine software engineering problem. Fundamentally
| you have some code that you want to run as fast as possible
| on the cheapest hardware you can feasibly use. Large
| companies like Google have the luxury of splitting this out
| into several distinct roles - from pure researchers (people
| publishing papers), to people who train models for business
| purposes (eg the Google Lens, computational photography,
| Translate), to people who optimise the ML library code
| underneath, to people who build out the end user application
| with the ML model as a black box service.
|
| Most of those people don't need to know much ML, but the
| exposure can help you transition into a more ML focused role.
| ccmonnett wrote:
| Especially if you have Python experience, then yes the
| opportunity definitely exists.
|
| For example when I hire MLEs (which I am doing now if anyone
| wants to apply - supportlogic.io) I am willing to look at
| people who are solid Python/backend engineers and who have
| been "ML adjacent" or who we believe could learn the ropes of
| ML enough to contribute. The stronger an engineer, the more
| flexibility we have in ML knowledge. Some ML engineering is
| task-specific but a lot of it is automation, data
| engineering, and improving data scientist code (for which you
| do need ML experience
|
| I've found it's a lot easier to teach an engineer enough
| DS/ML fundamentals to do ML Engineering than it is to teach a
| data scientist engineering skills. A _lot_ easier...
| anonymousDan wrote:
| Interesting. Honestly to me Python and backend engineer are
| effectively orthogonal skillsets though. I would expect any
| decent programmer to pick up Python in about a week...
| (slight exaggeration but you get the point).
| opensrcken wrote:
| The post is not only too braggadocious for my taste, but some of
| the figures quoted are highly unlikely. I would personally not
| work for someone with this kind of ego, but there are many such
| people in positions of power.
|
| This article is representative of an attitude I'm seeing around
| the tech industry, and if this is indeed the level of
| "confidence" in the Bay, I don't think that's a good sign.
| mysterEFrank wrote:
| Eric Jang is top ML talent, these numbers are accurate. I work
| in ML and have followed his work for years
| water-your-self wrote:
| He claims to be solving general intelligence in 20 years.
| Your advocacy is not enough to convince me.
| marginalia_nu wrote:
| General intelligence has been 20 years away since the 60s,
| along with fusion power and a bunch of other things.
|
| In marketing, they say 5 years when it's actually 20 years
| away.
| davnn wrote:
| Still.. do you think being a top talent in ML guarantees
| success for your own company, for example? I think there are
| a lot of valuable skills to have, being an expert in X is
| just one of them.
| blauditore wrote:
| > Low 7 figures compensation (staff level)
|
| Odd choice of level, since the author worked at one of these
| companies and was not at that level, certainly did not make 7
| figures.
| bwy wrote:
| They spent 2 years at the senior level at one FAANG. Why would
| they switch to another for anything less than the staff (senior
| + 1) level?
|
| (Not saying anyone "deserves" that or that's how it should be,
| but that's just how it is here in the valley.)
| usgroup wrote:
| I appreciate the post in the sense that it is an insightful
| perspective that he didn't have to share. If you are elite it is
| difficult to talk about your options, pay or way of thinking
| without it coming across as nothing but hubris to the rest of us.
| akhmatova wrote:
| _In the future, every successful tech company will use their data
| moats to build some variant of an Artificial General
| Intelligence._
|
| Is that what one gets paid 7 figures for - to go out in public
| and claim they "know" things like this with a straight face?
| jowdones wrote:
| oneoff786 wrote:
| People are hopping on this guy for saying he'll solve agi in 20
| years, but I'm already laughing at him thinking he'll be creating
| value with humanoid robots in 1 year.
| sfriedr wrote:
| vmception wrote:
| You forgot to write that crypto _startups_ compete with publicly
| traded FAANG on compensation on both cash and non-cash
| compensation, and there is no liquidity issue whatsoever on the
| non-cash they pay you with. Vesting schedules are _more_
| competitive than FAANG.
|
| And the publicly traded crypto companies compete with FAANG on
| compensation too.
|
| Non-crypto startups are the only ones sitting in the doldrums
| left out to dry right now.
| claytonjy wrote:
| This doesn't sound quite right to me; Coinbase pays well, but
| looking at levels.fyi it's not FAANG money; is there someone
| else you have in mind?
|
| Would love to hear more about the startups; I tend to turn down
| such opportunities far before we talk non-cash comp.
| vmception wrote:
| Levels fyi shows its FAANG money at all levels I have it
| listed alongside Google and Amazon and Facebook right now,
| what did you compare that seemed different? (They're not
| reporting consistent 7 figures for any of them) did you see
| something more granular?
|
| Outside of publicly traded crypto companies you need to talk
| to a third party recruiter in that space
|
| Solana Labs, for example, one of many, was paying engineers
| $650,000 back in 2019-2020 (and still is) to mostly write in
| Rust. Compensation was ~$200k cash and $1.6-$2 million in
| Solana tokens vesting 3-4 years with 1 year cliff. Solana
| tokens were $.10 cents back then, so those engineers are
| sitting on like $100 million+ as Solana trades at $100/sol
| now, down from $250/sol.
|
| For more typical results, companies that pay in crypto only
| have a few employees so giving them all a few million dollars
| in their much smaller less successful crypto still results in
| being able to liquidate close to the notional value they
| started with, derisking your time and coming out ahead in
| general. A "tiny" crypto is still like a $30 million
| marketcap. Even the $300 million marketcap ones are
| considered tiny. Market depth / liquidity is usually enough
| to support a few million dollars of periodic employee sell
| pressure.
| djenendik wrote:
| so you are saying there's a chance these solana engineers
| are sitting on a billion in crypto?
| sorry_outta_gas wrote:
| probably, that's what tends to happen when you literally
| make money
| vmception wrote:
| collectively? yeah, sure. this is absolutely probable in
| any organization of that valuation/marketcap, the most
| interesting thing here is just how fast crypto
| organizations can accrue and extract value.
|
| tech sector is fast, crypto subsector is like an order of
| magnitude faster. its similar to tech employment in the
| 90s where there was fast vesting (mostly due to quick
| exits), liquidity at super low valuations that then rose
| extremely quickly and attractive compensation. the main
| difference now is that the valuations are much much
| higher. you can tap in sometimes/often at very low
| valuations - of the token - and also ride them up all the
| way to billions valuation very quickly. if they solve a
| market need (within the crypto space) then they attract
| value very quickly, sometimes that market need can just
| be the entertainment coming from hype, but most times its
| bandwidth since there is not enough blockspace to go
| around, periodically.
| djenendik wrote:
| I'm just multiplying 1.6M by 1000. assuming a Solana
| engineer held on to every token they were granted.
| vmception wrote:
| Ah, nice, its possible. Yeah earning crypto has always
| been a greater way to make a lot of money quickly than
| trying to buy and trade cryptos, because there is no
| financial risk with your pre-existing capital.
|
| Team and advisor allocations have been this, and have
| been my best trades. Vesting grants for employees can be
| lucrative too. Often times these are also discounted
| prices to whatever any buyer can get. So things amplify
| very quickly, and there are less ways to lose.
| cellis wrote:
| It's important to keep in mind that working at the next
| Solana Labs, Alameda Research etc is roughly equivalent in
| probability as getting drafted in the NBA. That is to say,
| there aren't a lot of cases that happen.
| vmception wrote:
| I did say that
|
| > For more typical results,
| 1024core wrote:
| "FAANG+similar : Low 7 figures compensation (staff level),
| technological lead on compute (~10 yr)"
|
| I don't know where OP is getting these figures from, but I doubt
| that FAANGs offer 7-figure comps to Staff-level people. It's
| probably more in the higher 6-figure level (400K - 600K).
| ironrabbit wrote:
| The author is a skilled research scientist in a very
| competitive space with some high-profile publications (e.g.
| Gumbel Softmax). He is absolutely an outlier, but not a unicorn
| -- AI researchers with good publications and reputation will
| attract a lot of interest from companies with lots of money to
| spend. Low 7-figures for an ~L7 research scientist with
| competing offers from FAANG research labs is not crazy.
| greatpostman wrote:
| 600k is a senior engineers wage
| opensrcken wrote:
| No, it's not. If you get lucky with stock market movements,
| that might be your _total annual comp_ , which is not your
| wage. I made close to what you're claiming at senior level,
| at the recent _peak_ of stock market insanity, but I 'd stick
| to the dictionary definition of "wage."
| cellis wrote:
| This all day. Sure if you joined FB in March 2020 your
| total comp today will be a multiple of that 700k, but FB is
| not giving out 600k packages today to e5s ( and e5 imo is
| staff+ at many lower tier companies ), more like 450k all
| in compensation.
| bifurcations wrote:
| People are conflating SWE (or "research scientist" in name)
| bands with research scientist bands at labs like Brain. This
| guy is an outlier.
|
| "You can only be level X with compensation Y after Z YOE" is
| one of the greatest infohazards in tech.
| ren_engineer wrote:
| looking at levels.fyi it seems there are at least several
| companies paying 7 figures to ML engineers with less than 10
| years experience, although majority are at 15+
|
| https://www.levels.fyi/Salaries/Software-Engineer/Machine-Le...
| digitallyfree wrote:
| I'd be interested in a credible source for this as well, even
| though ML work pays more. From what I know from people in the
| industry it's 6 not 7 figures.
| ankeshanand wrote:
| https://aipaygrad.es/
| joshuamorton wrote:
| ML/AI initial offers can be fairly inflated
| (https://aipaygrad.es/)
| axg11 wrote:
| OP is probably in the best situation to judge this since they
| likely had competing offers or at the very least know peers
| with competing offers at FAANG+ staff level.
| redredrobot wrote:
| Currently on the job market in the AI space in the Bay Area -
| 400k to 600k is senior level at FAANG + similar. Low 7 figures
| at staff wouldn't shock me (although I don't have any actual
| data on that)
| opensrcken wrote:
| Pay definitely doesn't increase by 600k+ as you go from
| senior to staff. Yikes. Are people larping on HN?
| tmp_anon_22 wrote:
| Can we pause and admire a data scientist drawing conclusions
| while being utterly unconcerned with underlying data? ;)
| pvalue005 wrote:
| that's ok, they are bayesian
| [deleted]
| cjalmeida wrote:
| "Crypto community has weird vibes"
|
| that's what I call an understatement.
| axg11 wrote:
| One person's weird vibes is another person's great vibes.
| ozten wrote:
| Author is going to Halodi Robotics.
|
| I always thought that human shaped robots are a terrible form
| factor. Why limit yourself to the awkward design that 3.77
| billion years of evolution accidentally landed on?
| me_me_mu_mu wrote:
| Why do you think it is an accident? I thought evolution is an
| adaptation mechanism. If anything I'd say we've got a pretty
| cool form factor (peak human form, not like me who is out of
| shape lmao).
| pvalue005 wrote:
| The human body has been optimized for a very complex
| objective function, and in a very different environment to a
| robot. If you specify what the robots are doing, and the set
| of constraints like power source, size, weights, etc., the
| optimal design will unlikely be humanoid.
| ozten wrote:
| I used the word accident to emphasize that there is no design
| or designer. I agree that evolution is adaptive and often
| creates optimal solutions.
| riazrizvi wrote:
| Perhaps because that form factor has heavily informed
| civilization's current challenges?
| tuckerman wrote:
| There is a school of thought in robotics/AI that believe
| embodiment is necessary (or at least the fastest way) for us to
| learn abstract thought. Embodiment can really span the gamut of
| meanings, but there are definitely researchers that believe
| humanoid robots are the best path forward to that goal.
|
| If you have a more specific goal in mind, e.g. solving a small
| set of industrial/commercial use cases, that changes the
| calculus dramatically.
| sligor wrote:
| Human shaped robot is not a technical solution, it is a feature
| alfor wrote:
| Speed of iteration is all that matter.
|
| That is why all the big like Google no longer push things
| forward, even with the best engineer and Phds and with so much
| money.
|
| That's why I think that Optimus at Tesla will crush all the other
| robotic platform.
|
| On the positive side success of Optimus will help startups to get
| funds or get acquired by corporations that want to get a slice of
| the newly proven market.
| sidibe wrote:
| You are confusing speed of iteration with speed of
| announcements. There is a lot of stuff that happens, like
| research and process building, at a big "slow" company like
| Google that Tesla doesn't even realize is needed yet. Tesla
| makes a stream of wildly optimistic announcements that makes it
| feel like it's closing the gap with Waymo for example, but
| there's no evidence that it is
| alfor wrote:
| The iterations happen way faster too. Look at Munro teardown
| of Tesla, he has never see a tenth of that rate of change
| ever.
|
| Look at what SpaceX accomplished. Look where OpenAI is given
| the time it's been operating. Look at Tesla rate of
| production increase.
|
| It's going to be _very_ hard to compete with Tesla at this
| point. So much ressources, so much bright engineers, all the
| knowledge in manufacturing, all the training on vision, etc.
| sidibe wrote:
| I guess the difference is you think they have more
| resources, more bright engineers, and I'm quite certain
| they don't. I'd guess Tesla has several times less
| engineers working on FSD than Waymo and making much lower
| salaries, and they are spread across large parts of the
| stack that Waymo can just tap into Google for.
| TigeriusKirk wrote:
| Off topic a bit - "Halodi Robotics (company he's joining) intends
| to produce thousands of humanoid robots by 2023"
|
| Are humanoid robots just around the corner? Musk claims Tesla
| will have a "prototype" humanoid robot this year. I dismissed
| that as Elon hype, but have I missed this coming?
| throwaway889900 wrote:
| The only thing you need for a humanoid robot is for it to be
| human shaped and have some electronics inside of it. Doesn't
| mean it has to even remotely be human in interaction.
| captaincaveman wrote:
| The robots on their site look pretty clunky to me, not to say
| they aren't doing smart things.
| zcw100 wrote:
| After how many years of Facebook, instagram, youtubers, LinkedIn
| do people still not get it? This is the internet. How on earth
| can you verify the veracity of the authors statements? Everyone
| is discussing this like a blog post on themselves is an accurate
| portrayal of reality. It might be, but it might not. I'm not
| saying they're being dishonest, with they may be, but they are
| definitely not incentivized to give you an accurate portrayal of
| their life. This is instagram for IT nerds and there is no way
| they have a Badonkadonk that big.
| water-your-self wrote:
| Ah but its much more fun when everyone hosts their own blog.
| fartcannon wrote:
| To me, outside of this world, it feels like when one of the
| guys at a shop just like ours wins the lottery a few cities
| over. Everyone's just dreaming big for the fun of it.
| sydthrowaway wrote:
| This guy is another Dan Luu. Insufferable posts.
| jstx1 wrote:
| - This really isn't representative of the ML job market because
| the author is such an outlier.
|
| - The fact that it isn't representative is what makes the article
| an interesting read.
|
| - The fact that they claim to have a plan for solving AGI in 20
| years really detracts from their credibility.
| shepardrtc wrote:
| > - The fact that they claim to have a plan for solving AGI in
| 20 years really detracts from their credibility.
|
| I see they're going the cold fusion route.
| redredrobot wrote:
| There are some groups defining AGI in a way where a 20 year
| timeline is aggressive but not impossible. The real question is
| how is AGI being defined by the author.
| queuebert wrote:
| The problem with AGI is that it is people like this who are
| developing it.
| evrydayhustling wrote:
| On the plus side, the article accidentally produces the most
| useful definition I've seen of AGI. If you just define AGI as
| the the union and convergence of all hard problems, then you
| can just say "I'm working on AGI" to let people know you're
| doing the smartest, hardest thing, without sweating any
| details.
| hervature wrote:
| As someone who is in a somewhat similar position as the author
| (looking for senior ML roles), I found this part enjoyable:
|
| > I'm not like one of those kids that gets into all the Ivy
| League schools at once and gets to pick whatever they want.
|
| Followed by "FAANG + similar" and a deluge of options. Also, I
| feel like their message is pretty liberal with using future
| projections and implying it to be the present. For instance,
| the author has 6 years of experience with 2 at the senior
| level. This is pretty far from "staff level" (at 1M+
| compensation, I think this is L8) which they imply is/was an
| option at a FAANG company. I don't doubt that in 5 years they
| would be at that level, but they almost certainly did not get
| offered a "staff" position at a FAANG.
| cletus wrote:
| These outliers are rare but they do exist.
|
| I knew someone at Google who was hired as L3 straight out of
| college (as all non-PhDs are) and got promoted once a year to
| L6 (Staff) so 3 years. He got promoted to L7 2 years after
| that.
|
| It's a rare combination of talent and the right circumstances
| but it does happen.
| hervature wrote:
| I tried to hint at this by using quotes, I don't doubt that
| L6 is possible. But, please elucidate, are there L6s at
| Google making "low 7 figures"? From levels.fyi, there are
| no such reports. The average is about half and matches what
| I know from other companies. Those that are approaching 7
| figures have at least a decade of experience. Anyway, the
| pay he describes is much closer to L8.
| deep_etcetera wrote:
| You can see some examples here https://aipaygrad.es/
| [deleted]
| [deleted]
| titanomachy wrote:
| FAANG staff in 5-6 years out of school is not impossible. I
| know a couple. They are significant outliers in terms of
| focus, dedication (i.e. hours worked), and raw intelligence.
| If I had to guess, I'd say 1 in 30 from the population of
| Google-level engineers.
| krat0sprakhar wrote:
| > They are significant outliers in terms of focus,
| dedication (i.e. hours worked), and raw intelligence.
|
| As someone who has worked at FAANG for 5 years right out of
| school, getting to staff is less about raw intelligence and
| more about being lucky with working on projects that did
| not get canned and finding supportive managers. My friends
| much smarter than me have not had a good growth purely
| because they were unlucky with initial team assignment and
| PA / reorgs cancelling their projects.
| davnn wrote:
| Is there a large org where commitment/getting things done
| is more important/valued than social skills/network/luck?
| I.e. is there a fair model to measure an individual's
| contribution?
| jstx1 wrote:
| It's ambitious bordering on delusional. They're also doing
| the dirty trick of putting "2016 - 2022 Senior Research
| Scientist at Robotics at Google" on their resume even though
| they've been in the senior position only since 2020. Like,
| dude, you're doing great, your resume doesn't need any more
| artificial pumping up. Or I guess it does if you're aiming
| for those positions that are kind of out of reach.
| colonelxc wrote:
| I don't think that is unusual to list the latest level on a
| resume. I'm certainly not going to dedicate space on a
| resume to list time ranges for every promotion.
| jstx1 wrote:
| So if this person had been promoted to staff level in
| 2022, they could have changed their resume to "Staff
| Research Scientist, 2016-present" and that would be okay
| with you? Because it seems deliberately misleading to me.
|
| It's different if the level isn't represented in the
| title - if they went from one band to another but the
| title was the same, I don't see a problem with putting
| down something like "Software Engineer, 2016-present"
| without wasting space on each promotion.
| colonelxc wrote:
| In general, yes, I think that would be okay. I think it
| would be a mistake to create separate sections for each
| level. Overall your achievements within a single company
| should not be organized chronologically, but by what you
| want to show off. This may still be mostly chronological
| as you take on more responsibility/leadership.
|
| Now, I don't object to adding a line like: "promoted
| twice from Software Engineer 2 to Staff Software
| Engineer" or whatever, which I think is a good middle
| ground (and I would put this as the very last, least
| important entry for that company)
| uoaei wrote:
| But it undeniably misleads the reader into thinking this
| person has held senior _responsibilities_ since 2016,
| which is outright false, and may instill in the reader
| more confidence than is due.
| pbronez wrote:
| As a hiring manager, I don't assume that. When there's a
| single title over a long range of time, I assume it's a
| terminal title. I look to the details for the position to
| see what kinds of work they've done. In the interview,
| I'll dig into trajectory and experience at various
| levels.
| uoaei wrote:
| Then how am I to communicate to you, the hiring manager,
| that I held significant responsibilities for a longer
| period of time (eg 6 years) than an applicant who ducked
| out the moment their new title kicked in (eg 6 weeks)?
|
| How much does that matter to you, anyway?
| mbrameld wrote:
| I'll add another data point in opposition. I expect to
| see only the latest title for each company on a resume
| and my resume is organized the same way. It's practical,
| not deceptive.
| toddm wrote:
| Ironically, he graduated from Brown University, an Ivy League
| school.
| ZephyrBlu wrote:
| And he worked in one of the most exclusive teams, Google
| Brain.
| toddm wrote:
| Kids who graduate from Ivy League schools get a lot of
| job offers at once and get to pick whatever they want.
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