[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. ___________________________________________________________________ (page generated 2022-04-25 23:01 UTC)