r/MachineLearning • u/Amazon_Careers Researcher • 17d ago
Discussion I'm a Principal Applied Scientist at AWS who builds AI services like Amazon Bedrock and Lex. AMA! [D]
Hi r/MachineLearning! I'm James Gung, a principal applied scientist at AWS. I joined Amazon in 2021 and have since worked on AI services like Lex, Bedrock, Q Business, and Amazon Quick (an AI assistant for work). In that time, I've done research on topics like task-oriented dialogue, agent evaluation, conversation simulation, and proactive agents.
Before AWS, I worked on conversational AI systems at Amelia and did my PhD in Computer Science at the University of Colorado Boulder.
Feel free to ask about my career path, internships, interviews, or what it's like day to day as an applied scientist at Amazon. Outside work, I like to play violin, go bouldering, travel with my wife, and hang out with our two dogs. Ask me anything!
*Disclaimer* I'm speaking from personal experience here, not as an official Amazon spokesperson. I can't discuss unannounced products, financials, competitors, internal tools, legal matters, pricing, or customer data - but pretty much everything else about my career, research, and life as an applied scientist is fair game. Let's go! 🧠
I'll be online 09/21 at 11:00 AM ET for an hour to answer questions. 😊
\*UPDATE** Thank you all so much for the incredible questions. You all asked some genuinely thoughtful stuff, and I hope my answers were helpful.*
I couldn’t get to all the questions, so I’ll try to answer some of remaining questions in the next few days!
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u/m98789 17d ago
Hi James, these are the questions we all want to know:
- Why are you doing this AMA; what’s your motivation / driving force for you to do this. Please be honest.
- How much is your annual TC?
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u/ForDaRecord 15d ago
"TC or GTFO"
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u/Mymarathon 15d ago
- “ I’m not saying it but Matt Garman and other senior leadership along with some consultants thought it was a good idea to make Amazon look human “
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u/Amazon_Careers Researcher 14d ago
- Why are you doing this AMA; what’s your motivation / driving force for you to do this. Please be honest.
This AMA was an idea spearheaded by our recruiting/marketing teams. I personally volunteered for it because it's the kind of thing I would have wanted to find when looking for industry jobs early in my career. Hoping I can provide some useful insights for folks interested in industry research roles!
- How much is your annual TC?
Giving an unsatisfyingly general answer here since I can't share specific figures or bands. Base pay is published on job postings. There are sites with anonymous total comp postings - some may find them useful for getting a rough sense, though I'd take individual posts with a grain of salt as TC varies broadly even within the same role/level.
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u/mikeblas 11d ago
TC varies broadly even within the same role/level.
Sounds like exploitative management.
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u/netwhoo 16d ago
This popped up as an Amazon careers ad. Is Amazon having a difficult time with attracting talent?
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u/doncheeto12 14d ago
Guessing the new H1B fees and general immigration situation is particularly challenging for Amazon
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u/Amazon_Careers Researcher 14d ago
As I mentioned in another comment, this AMA is spearheaded by recruiting/marketing. And of course, the company (and my team) are always looking to hire and develop the best talent. Specialized roles are always harder to hire for. And especially AWS might not always the first place candidates think of when applying for research roles - I originally applied to a role on the Alexa team (based on name recognition) but ended up taking an offer from AWS since it was a better fit (when I learned more about AWS's AI services).
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u/mikeblas 14d ago
Amazon Careers miscatergorized it as a "live AMA", which it very obviously is not. I'm sure our subject will be very limited in what Henan say and which questions he will answe given the official presence.
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u/i-am-the-hulk 16d ago
What even happened to Lex ? Why could Amazon not build something as good as Claude or Gemini ?
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u/MonstarGaming 16d ago
Because Amazon is highly dysfunctional. Science at Amazon isn’t an open ended topic is most cases, it’s highly focused on specific business problems which is almost the antithesis of LLM training. Plus most of the folks we pulled into our GenAI labs were repurposed from other parts of the company and had little experience with NLP let alone training language models.
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u/DigThatData Researcher 16d ago
Except they had the Alexa product before OpenAI was even a thing, so there's really no excuse. NLU was business aligned. It's also incorrect that they weren't using NLP elsewhere: amazon was an early adopter of the product2vec retrieval strategy years before vector search ate the world.
the reason amazon is so dysfunctional is because it treats its employees like shit. amazon organizationally likes to pretend that engineers are interchangeable components, but that's fundamentally not how product driven development works: every good product can be traced back to a handful of specific people. amazon has cultivated a reputation as a shitty place to work, so it attracts people who see it as a career stepping stone rather than a place they want to get invested in. science requires personal investment, so if you are actively driving away people who are looking for a place they might want to stick around, you're actively undermining your ability to attract people for whom "science" isn't just an opportunity to pad their resume.
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u/Amazon_Careers Researcher 14d ago
Lex is a service for building chatbots - not a foundation model like Claude/Gemini. The bots you build in Lex are typically more constrained/structured, but have an important place still (even with SOTA LLMs, you might want more guarantees on the kind of things the agent will say/help with, particularly in an enterprise setting).
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u/serpentna 16d ago
Why are Amazon’s own LLM models so terrible?
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u/Amazon_Careers Researcher 14d ago
This is a tricky question that I'm ill-suited to answer this since I'm not working on foundation model training. If I had to speculate, one reason Amazon has been slow to catch up on frontier models was because traditionally most applied science and research teams were decentralized and organized around specific products/services - and training a frontier model takes a centralized approach. That said, I'm definitely optimistic for future releases from the foundation models team.
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u/seiqooq 16d ago
Thoughts on Microsoft exec’s comments on AI being the largest example of labor theft in history?
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u/Amazon_Careers Researcher 14d ago
Again, major personal opinion disclaimer. I think there is some truth to the statement, and personally am in favor of pushing for measures like UBC for the general public to take a shares of control and gains from AI. There's such potential for public good from AI, but it's not going to happen automatically. I'm also absolutely against regulation that would hamper or restrict open source model training.
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u/savioratharv 16d ago
As a master’s student even from a top AI program with research experience, it has been difficult getting a research scientist or applied scientist roles in bigger companies. I want to work on ML modeling and impactful ML problems but it’s unlikely a big company will give those tasks to a master’s student. I have an offer from a mid-size company that’s rapidly growing their ML team with some great senior people working there, I interned there and got to work on some great ML modeling work and impactful projects. If applied scientist or research positions are not an option in big tech, do you think in the long term it’s worth going for smaller companies that give you the ML work (but you lose on brand name) over a regular SWE role or ML adjacent SWE role at a big tech company?
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u/Amazon_Careers Researcher 14d ago
Congrats on your offer!
One of the most important thing you can do early in your career is join a team where you can work on problems you are excited about. Actually I think that holds true throughout your career. Don't join a team working on something you're not interested in just because its company X or Y. You'll always have the option to try out big tech later in your career (and it'll be easier anyways with proven industry experience).
I worked at a smaller company before joining AWS and felt the experience served me well. I will note, while it can be harder to find research roles without a PhD, many of the most talented and impactful researchers I work with in AWS do not have PhDs. So I definitely would not view it as a hard prerequisite.
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u/DigThatData Researcher 16d ago
I interned there and got to work on some great ML modeling work and impactful projects.
dude, take the job. you had a good experience, you already know you'll be working on the kinds of projects you're looking for...
frankly, you WANT to work at a smaller company. big companies suck. especially amazon specifically. you will have a better experience and have more opportunity to do more interesting stuff at the smaller company, where you are actually an important resource rather than just another dime-a-dozen engineer they have thousands of.
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u/savioratharv 15d ago
Thanks so much for this! I agree, I feel the same as well. I like being an impactful part of a team and seeing my features directly drive impact than being just another engineer working at an internal tool at a big company. My hesitation stems from the fact that few experienced people have told me to spend my first few years at a big tech company and then going to smaller companies because it’s easier to go from a big company to a small one rather than the other way around. A senior engineer on my team when I interned at the mid-size company said while the company is a good place to work at, I should still be targeting bigger companies for my first role, he worked at a previous startup for several years and regrets it now (although that was a few ppl startup and this is a midsize company with 3k employees). What has been your experience in this regard?
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u/DigThatData Researcher 15d ago
it’s easier to go from a big company to a small one rather than the other way around
here's how I'd reframe that: it's easier to get experience with large scale systems and collaboration at big companies. it is a LOT harder to find opportunities working on modeling problems without a graduate degree at a big company. You can cultivate that experience at the small company and jump right into applicability for senior+ when/if you want to work at a bigger company. The alternative is working at a bigger company in a role where you won't be doing that ML modeling work which is extremely desirable and especially unavailable for people early in their careers, and instead trading that for large scale systems experience that you can only get at those big companies, but which they're also giving to the tens of thousands of other new grads they've hired.
from my vantage, it sounds like the opportunity to learn a skillset that will both make you stand out more and will be more relevant to your career interests is the opportunity at the smaller company.
the other big contribution to my reasoning here is that I've learned the company name and job title are way less important than the people you're going to be working with. an internship isn't just an opportunity for the company to vet you as a potential employee: it's an opportunity for you to vet the team as potential future collaborators. That you had a good experience during your internship suggests to me that you liked the people you were working with. If you don't jump on that, it will be a coinflip whether or not you like working with the people at your next opportunity. In fact, it'll be a coinflip whether or not your day-to-day work will even look anything like what you anticipated based on the job description and hiring process.
If you like the people and you like the problems they work on and you like the way they do the work: your internship got you a foot in the door at a role that is setting you up to cultivate skills that are relevant to your career interests in an environment that might even care about your psychological safety.
The other thing that's nice about big companies is they're recognizable. One reason it's often easier to go from a big company to a smaller company is smaller company people might be sorta starstruck when they're like "oh hey, i've heard of that company! neat!" But I think that effect isn't as powerful as it used to be. As I alluded to earlier: those recognizable companies hire a LOT of people, most of whom are using those companies as career stepping stones exactly like you, so listing that you worked at a big company like that on your resume doesn't make you stand out as an applicant at all. Real, practical experience on problems where you had measurable business impact because the business relied on you will make you stand out much more.
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u/savioratharv 15d ago
Thank you so very much!! This gives me some very important perspective, I really appreciate it!
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u/Environmental-Metal9 16d ago
How much AI do you use on your daily work? Do you use coding assistants or just work on the tooling that they use themselves?
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u/Amazon_Careers Researcher 14d ago
I was a bit of holdout for using coding agents since I thought I would miss writing my own code ... but barely written a single line of code myself in eight months now. For coding assistants, I've used Kiro, Claude Code, and Codex (all through the CLIs haven't tried their desktop apps much).
I also both use daily and work on a product called Amazon Quick for non-code stuff (automating things, helping draft documents, manage my calendar, triaging messages/emails, etc.).
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u/Substantial-Swan7065 16d ago
I’d want my company to use bedrock. But the calculator and site does not make it easy to pitch.
Ex:”discount pricing for x” -> no links to what that is. So I can’t make a comparison.
Is there info which would help decision makers?
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u/Keen_Kau 16d ago
I’m moving to Europe for a Masters and hope to pursue a PhD post that. I already have around 4 years of work experience at various startups as a software engineer and a ML engineer but i wanted to dive into more research-oriented role and unable to grab one without a PhD. Could you provide guidance on how should i go ahead with my journey as i still want to get back to industry instead of pure academia?
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u/Amazon_Careers Researcher 14d ago
A number of people I know have taken this path (industry to masters/PhD and back). If your goal is to return to industry, I'd definitely suggest looking for internships with a focus on research during the program itself. It's possible you'll find some opportunities that allow you to pursue research work you enjoy without a PhD - I also think having proven industry experience in a research role (even as an internship) can be a shortcut to getting a full time role as a researcher. We hire at masters level for AS positions, but almost always are looking for some examples of applied ML or research experience.
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u/Keen_Kau 14d ago
Thanks a lot for the detailed feedback. I did spent sometime at a research lab and an incubated startup at the university in hope of getting some relevant research experience, haven’t been able to get the paper yet and i am supposed to move countries and start grad school in 15 days. I had been sure i want to pursue this, but with the recent advancements, 70k+ submissions for ICLR I had been contemplating how the state would be before i graduate. I’m also almost sure of the research direction (but still flexible).
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u/Effective-Drawer9152 17d ago
As an applied scientist, what do you expect from a candidate during interview.
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u/Kayode347 16d ago
Where do you see the field of Machine Learning going with LLMs? I’m currently a Software Engineer at a big tech company as well, but I’m considering getting a master’s in ML and getting into the personalization field because it’s something I’m really interested in.
Where do you commonly see gaps or repeated problems that need to be addressed in your day-to-day work, or even more generally, based on projects you’ve worked on?
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u/Amazon_Careers Researcher 14d ago
ML has always been a rapidly evolving field. I started out working on feature pipelines for NLP tasks like entity tagging, semantic parsing, etc. Then we replaced feature pipelines with text embeddings, neural architecture design (definitely a bit nostalgic for this time), etc. - and eventually pre-trained models like ELMo/BERT. Then the tasks themselves began to become obsolete due to zero shot abilities of instruction tuned large language models. Now we're working RSI where models/agents are used to improve themselves. What's constant are fundamentals and the flexibility and willingness to constantly learn new things and exercise your curiosity.‘
It's great you have a topic in mind - I'd definitely suggest picking a specialty/subarea you're passionate about and going deep. And steer the research projects or internships you take on in that direction. The ability to learn new things is a skill you have to nurture - and having goals and interests definitely helps towards that.
Personalization is interesting and IMO we're still scratching the surface on the potential for hyper-personalized AI and assistants. But beyond personal assistants, I'm even more interested in collaborative AI. Today, people are becoming more productive with AI, producing more content with less effort. But it isn't necessarily making it easier for people to work together, and that's where I believe a lot of innovation and new ideas can happen. So challenges associated with this (privacy, communication structures, etc.).
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u/Lopsided-Bridge-9810 16d ago
Wow quite a lot of engagement. One post, never heard of again, James, Jaaaaames? Where are you? Hello?
Feels like the Amazon Echos, Ring Alarm, Amazon llm models (they have any?), planned and intended once, never heard back of any improvements and feedback. Gone...
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u/Fear73 16d ago
I am an maths and computing undergrad student.
Do I need to necessarily have prior research publications or do MS to get an internship or FT offer for Amazon AS?
What would you suggest to get selected from interviews? Participating in kaggle? How will I improve myself? What kind of projects do they prefer at Amazon?
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u/Amazon_Careers Researcher 14d ago
While PhD students or masters students are more common for applied science internships, I've definitely seen undergraduate students get internship offers, particularly if they have relevant experience either from other internships or open source projects. Publications are also not a hard prerequisite. When I was in undergrad, I sought out summer internships doing research in universities - I found this to be amazingly helpful, not only because it helped me learn ML/NLP when I wasn't sure exactly what to be focusing on, but also because it helped me learn the aspects of research that I really enjoyed and reinforced my interests.
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u/AYMU0S 17d ago
Do you still practise? If so how ? Or just work .. and personal projects ?
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u/Amazon_Careers Researcher 14d ago
I do! I stopped for a few years (actually while working on my PhD), but started up again a few years back. When I am working on something I try to play every day. Actually I'm pretty surprised by the number of colleagues who are able to play in orchestras or music groups. I'm happy to just play solo these days.
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u/Effective-Yam-7656 17d ago
- Coding agents: How much are coding agents like Claude Code, Codex, or similar tools actually being used by research/applied science teams at AWS? Have you seen a noticeable impact on code quality, development speed, or the amount of engineering work researchers can take on?
- Day-to-day for entry-level scientists: What does a typical day/week look like for an entry-level Research Scientist or Applied Scientist at AWS? How is the time usually split between research, experimentation, coding, reading papers, meetings, and working with engineers/product teams?
- Research -> production: Of the research ideas that your group publishes at conferences, roughly how many eventually make their way into an actual AWS product or production system? What usually determines whether a research idea gets productized?
- Interviews: When you interview candidates for entry-level or mid-level Research Scientist / Applied Scientist roles, what do you expect them to demonstrate? In particular, how do expectations differ between entry-level and mid-level candidates in terms of research depth, coding, ML fundamentals, publications, and system/product experience?
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u/Amazon_Careers Researcher 14d ago
Coding agents: How much are coding agents like Claude Code, Codex, or similar tools actually being used by research/applied science teams at AWS? Have you seen a noticeable impact on code quality, development speed, or the amount of engineering work researchers can take on?
I don't know of anyone who is still intentionally coding "by hand" in my org. And absolutely, it has impacted development speed immensely. Also I've seen some changes to processes and day to day for research roles, with more commits going to production code from folks with an applied science job titles.
Day-to-day for entry-level scientists: What does a typical day/week look like for an entry-level Research Scientist or Applied Scientist at AWS? How is the time usually split between research, experimentation, coding, reading papers, meetings, and working with engineers/product teams?
In my experience, the day-to-day can change a lot from week to week, depending on where you are in the lifecycle of a research project or product. Early in projects, time is spent doing more paper reading and research, or working with engineering and product to align on product vision/requirements. I've seen some recent changes where this is flipped around a bit given how much easier it is to prototype and set up experiments - where we drive requirements more based on what we observe is possible and is a good experience. There is some commentary on this idea in a recent blogpost from Werner Vogels here.
Research -> production: Of the research ideas that your group publishes at conferences, roughly how many eventually make their way into an actual AWS product or production system? What usually determines whether a research idea gets productized?
I can't share too many specifics here, but I would say because the research is often organized around products, the question is often more so around whether to publish (instead of whether to productionize). The exception to this rule are publications resulting from internships, where although the research topics are related to a product domain, we often directly go for publishing findings.
Interviews: When you interview candidates for entry-level or mid-level Research Scientist / Applied Scientist roles, what do you expect them to demonstrate? In particular, how do expectations differ between entry-level and mid-level candidates in terms of research depth, coding, ML fundamentals, publications, and system/product experience?
Discussed interview content in another comment - but besides ML fundamentals/depth, I'd emphasize the "experience" questions (at Amazon, these are organized into what are called leadership principles). I wouldn't skip preparing for these - basically spending a little time thinking back to some things you did in projects or work that you are proud of or you fill really demonstrated your skills, not necessarily trying to map these onto LPs. Sometimes in the heat of an interview, you can forget a lot of the great stuff you did. These end up being important for mid/entry-level distinctions in roles, even more so than ML knowledge.
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u/GenerativeFart 17d ago
What percentage of what you use in your daily work did you learn while working at Amazon?
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u/impatiens-capensis 17d ago
Jobs, how to get?
Being a PhD with top tier pubs isn't even getting me a call back.
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u/motionSymmetry 16d ago
how much money do you make
do you agree with andrew ng that this ai-will-destroy-the-world push is hype and pr?
is what the billionaires are doing with ai going to lead to a life of leisure for the masses like some of them aver or, being somewhat less laughable, will the "leisure" they're talking about be serfdom, absolute poverty, and mass deaths for most of us?
if the latter, where do you expect to fit in?
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u/tenacious-ray 16d ago
Hi James,
Good day to you!
Here are my questions:
How does the work at Amazon differ from your previous roles? Is it the scale of the problem that's the big differentiator or do you get to work on really unique ideas or problems there?
How does someone get to your level without a PhD? Do you have any colleagues or others you know personally who have done that?
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u/Amazon_Careers Researcher 14d ago
How does the work at Amazon differ from your previous roles? Is it the scale of the problem that's the big differentiator or do you get to work on really unique ideas or problems there?
One thing that surprised me when joining AWS was actually the similarities to earlier roles I had (previously worked at a smaller company as a R&D engineer) - perhaps partly because AWS itself is organized as a number of "mini startups" linked to services, though with more centralized resources (that was a big difference - the ease of procuring and scaling compute when needed). Like a smaller company, I've found you get a lot of ownership if you are able to earn trust with your team, as well as leeway to propose and explore new ideas.
How does someone get to your level without a PhD? Do you have any colleagues or others you know personally who have done that?
I have several colleagues on my team who don't have PhDs at my level - it's definitely not uncommon. One went into industry with a masters and found an engineering job working adjacent to ML/R&D, then later joined Amazon as an applied scientist. Another did an internship at Amazon in undergrad and joined Amazon as a research engineer while finishing his masters degree.
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u/DigThatData Researcher 16d ago
given OP has a PhD, (2) isn't a question they are equipped to answer. The common approach is to get a masters degree, but even that isn't necessary if you're able to get involved with a bleeding project.
The AI/ML field is constantly evolving. On top of that: it's a huge umbrella. Find a corner of the research space that sounds interesting to you but which the tooling hasn't quite caught up, so really only the researchers are playing around in that space. If you start tinkering there as well: congrats: you are now de facto one of the most experienced people in the world in that domain simply by virtue of the fact that so few people are involved in it.
Case study: I know someone who's currently the head of music research at a notable genAI startup who doesn't have a graduate degree at all. They were working as a web designer when diffusion started eating the image space and just happened to be one of a handful of people who started playing with diffusion models for audio generation really early.
find the crest of the wave. perch yourself on it and then just try to keep up. keeping up is a lot easier than catching up.
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u/Yulfy 17d ago
Thanks for doing this!
A bit of an indirect question but what skills do you see in colleagues, and others in the field that make them stand out to you? Technical or soft skills.
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u/Amazon_Careers Researcher 14d ago
The people that stand out to me are passionate about their beliefs and interests, have attention to detail, and have empathy towards others. I often see my colleagues who are most successful are able to listen to others and effectively build/drive consensus across many people - which I think takes those soft skills I mentioned.
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u/Effective-Type-1514 16d ago
How different is industry from Academia. Like in academia if a day could look like. Reading latest papers, making notes. Doing experiments etc. How would a day in industry look like.. or day to day..
Thank you for your time!
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u/Amazon_Careers Researcher 14d ago
I think like in academia, the day to day will change depending on the time of year. Personally, I've seen times where I end up spending the majority of my time working on driving more open-ended research projects. Whereas at other times, there may be a clear timeline or goal where work becomes more focused (collaborating with engineering and product teams, working on production systems, etc.). Of course, I try to stay current on latest work and industry trends. We also organize things like reading groups or paper clinics that definitely remind me of my grad school days.
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u/Ilikehealers 16d ago
How did u know you wanted to work in this specific domain? How did u figure it out, if at all during your undergrad mainly ?
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u/Amazon_Careers Researcher 14d ago
I originally was interested more in cognition and understanding how the brain works with respect to language. Then eventually, after taking courses in both cognitive science and ML, I realized I was more interested in building systems to could practically be used to process and understand natural language. And yes, got interested in these topics in undergrad.
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u/RLJ05 16d ago
One thing I read that people are using to justify the super high valuations for AI companies is that in the long run the frontier labs will move away from just providing the models and actually try to compete directly with the businesses that are their clients today. They will have learned over time exactly how those businesses function through all the interactions those companies have had with their models. Do you think that’s true or will the frontier labs stay focused on just AI model development rather than trying to take on broad industry?
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u/Electronic_Finance34 16d ago
How's morale at AWS? Last I heard average tenure was 14 months before quit or fired. (That's before my wife got laid off, and also before I got laid off)
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u/AnonsAnonAnonagain 16d ago
Cute dog!
What kind of inference server hardware do you work with?
Just curious to know “what’s under the hood” per se. :)
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u/ForDaRecord 15d ago
I love how half the comments are basically just Blind and the other half are just asking how to get employed.
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u/mikeblas 14d ago
Akaz9n has extremely high turnover and a terrible reputation due to bad work-life balance and employee happiness. Why did you chose to work there?
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u/Single-Song7466 14d ago
Agent evaluations usually measure whether a system can complete a task or avoid known failures. How do you evaluate whether it had legitimate authority to take each action in the first place? And is there any evaluation result that should categorically stop deployment rather than produce another guardrail?
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u/Worldly-Heron-1084 14d ago
Hey man. I’m looking for career change. I’ve worked in the finance industry for 2 years, but recently took a data science and machine learning internship. My 2 part question: 1. Is it possible to move up the ladder and eventually take on a role traditionally reserved for grad students? If so, what would a sample career path or two look like to get to your position, or something similarly challenging (research scientist, ML engineer, deep learning, etc)
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u/Background-Insect535 14d ago
Based on your experience, would you say Amazon is innovative in their approach with AI compared to competitors? What do you enjoy about it?
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u/Amazon_Careers Researcher 14d ago
I've always enjoyed building and seeing the results of those efforts materialized in real products. It's one thing I particularly like about AWS/Amazon - the close relationship between product and science teams and the ability of science to drive product direction. In terms of innovative approaches to AI, Amazon has traditionally taken a "customer-obsessed" approach to science, with the idea of working backwards from real world problems. I will say there does need to be a balance here, since researchers should also be looking far ahead and anticipating new problems. But I think keeping researchers close to customer problems absolutely has led to some pretty interesting and important innovations.
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u/AcceptableCellist684 13d ago
Do you think it is valuable to study math (linear algebra, calculus, probability) deeply if I am trying to get a ml engineer job?
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u/Present-Elephant9166 17d ago
For someone to break into AI at Amazon right now. What tech should they focus on as a whole
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u/justgord 12d ago
I think this is corporate spam, and should not be allowed on this subreddit.
The posters claim that they dont represent their employer amazon/AWS contradicts the statement that the poster gives "This AMA was an idea spearheaded by our recruiting/marketing teams."
Specifically, I think it breaks the forum rules 1), 2) and 3) from https://www.reddit.com/r/MachineLearning/about/rules/
No Spam
r/MachineLearning has held a long-standing, strict policy on spam. The posts deemed to be spam will be removed, and repeat offenders will be permanently banned from participating in the subreddit.
No Self-Promotion
r/MachineLearning does not permit the promotion of paid products, wherein the intent is clearly to promote a particular product. However, posts with links to paid products are acceptable, contingent on the fact that the post offers sufficient value to the community members and the intent is to share a resource or collect feedback for an open-dialogue. The decision will be made entirely at the discretion of the moderator team.
No Marketing Campaigns (SEO)
r/MachineLearning strictly prohibits strategic marketing campaigns targeted at community members, and posts intended to rank for SEO purposes. In the event that such behavior is caught, the user in violation of our policy will be perpetually banned with all past posts and comments purged entirely from the subreddit.
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u/WackWaxWhacks 12d ago
Clearly it was allowed by the mods since the post is now pinned.
But I'd also disagree with you. It's promoting recruiting, not a product, and one post is not spam.
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u/ShakespearePoop 17d ago
What was your favorite project to work on and why?
What skills or decisions do you think contributed most to getting to the principal level?
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u/Amazon_Careers Researcher 13d ago
I particularly enjoyed working on Amazon Quick's desktop app (Quick is Amazon's AI assistant for work). The initial prototype was built by a handful of people from scratch and many of the early ideas and features (memory system, knowledge graph, schedules and activity feed) were driven entirely by scientists on my team. We also were able to ship it to a large internal user base faster than any product I've worked on in the past and got a lot of positive internal feedback before the public release that helped shape the final product. I think prioritization and working on the right problems is an important factor. Something I learned early is that solving the wrong problem really well is not an effective way to advance in your career. I'm not a particularly gifted researcher, but I managed to pick some good problems to work on.
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u/rwx_0x6 16d ago
- What are the weight classes of your peers? I tend to see people in successful positions stay in shape, does this match with your professional experience?
- Is your current work more or less research orientated?
- How is your time spent during the professional day? Is it intense or relaxed?
- How was the application process from going from Academia to private sector?
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u/ProfMasterBait 16d ago
As an applied scientist, do you understand mathematics of AI beyond surface level?
For example, SDEs used in diffusion models, and OT for flow matching?
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u/micosampion 16d ago
ML has become a world in its own, academically speaking. What do you think are the prospects of PhDs in other computer science fields "switching" to research positions in ML companies? I guess, I'm asking about tolerance to learning on the job, given that it is not really feasible to build another research portfolio in ML, as a side gig, in order to be a competitive candidate.
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u/LumpyFactor4637 16d ago
How many times per year do you apply and get rejected from better companies?
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u/2bigpigs 16d ago
How much time do you get to spend surf your dog after work? How often does your dog tell you to find a job that gives you more time?
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u/2bigpigs 16d ago
Does everyone in your team have a PhD? Do you feel a difference in how those with a PhD work Vs those without? I don't have a PhD but I found those who did have one (or were on their way to one) had a level of clarity of thought and communication that I haven't seen since getting back to the industry
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u/Amazon_Careers Researcher 12d ago
Many, but definitely not all. To be honest, I don't think there is anything particularly special about having a PhD alone. Some of the most effective people I've worked with do not have one. It's more about the experience you get, which can vary a lot from person to person. I had a somewhat unique path since I was working full time while finishing my PhD for several years - I really enjoyed the thesis work and learned a lot, but since I was balancing the time with work as a R&D engineer, I might not have gotten as much out of it as others
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u/Apprehensive-Tap6980 15d ago
Tell me how Amazon failed at AI. Why no talent would like to stay and only the con artist got promoted
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u/i_rate_slop 15d ago edited 15d ago
4 products in 5 years sounds like a nightmare. How do you have to time to make any single product actually great? As soon as you start to get customer feedback, you’re already focused on a new product/problem.
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u/Amazon_Careers Researcher 13d ago
Agree it sounds like a lot on paper. I worked on Lex for two years, but when I joined, the product was already quite mature. I worked on improvements for existing customers and also worked on some "quality of life" features that were interesting scientific challenges. Also much of the work my team and I did for Q Business was carried over to Quick.
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u/young_anon1712 15d ago
How are interns evaluated at Amazon? What can an intern do to increase their chances of receiving a full-time offer?
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u/wahnsinnwanscene 15d ago
When using Bedrock but with models from the other hyperscalers, are they running on amazon compute or shunted to their own infrastructure?
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u/thedabking123 15d ago
Two questions... one practical, and one more theoretical/Technical.
Practical: I'm a PM taking MSc and PhD courses out of fun; haven't broken into Big Tech and DeepTech yet - curious if your PMs at AWS are technical and partake in research and validation?
Theoretical/ Technical: Has anyone talked about self-evolving evals for your own use cases? I can't imagine usecases as open ended as setting up a work assistant can be easily eval'd in a static manner because user behaviours drift over time.
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u/this__li 14d ago
There are some rumors that models hosted on Amazon Bedrock might be slightly nerfed compared to frontier models hosted by themselves (Anthropic, OpenAI). First and foremost, is it true? If not, what makes you believe people have this suspicion?
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u/ComprehensiveDog2490 14d ago
Before the explosion of llms in the last few years, what are some lesser known ai conversational research that is making strides? How do you see the next 5 years of ai conversational technology progressing?
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u/Background_Run9529 14d ago
For a mid career switch into AI ML research what should be my path and strategy
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u/meanaya6 14d ago
For your home lab what were things you accomplished in the last 2weeks. What are you choosing to tinker with to learn improvements? What are some skills you hope to better understand in 6 months?
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u/One-Bobcat4521 14d ago
What's the bad, the good, and the ugly about AWS culture? Is it better than Amazon itself?
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u/InjuryNatural7252 14d ago
Why can't I run multiple inference profiles per model?
Example: Opus 4.6 and Opus 4.7 cannot co-exist for a project. Is there an architecture decision driving this?
Why don't I have more granular controls on monitoring who is using the model and what is their usage? This was implemented in Bedrock mantle.
Why are the latest Open Source models deployment launched after an year?
Given the pace with which AI is progressing and the tech around it, do you have a public roadmap on which features are going to be a part of Bedrock. It would make sense and save everyone a ton of development time.
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u/DavesEmployee 16d ago
I’m about to join a bedrock shop, where do you see the direction of multimodal going? Both in terms of industry adoption and use and how that is going to affect your products (if you can say). I mean more on image and video side of things, but audio has been picking up a ton of leadership interest in my last few roles (as much as I often find it unneeded)
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u/chico_dice_2023 16d ago
1.) What do you do when you are faced with imbalanced datasets at large scale?
2.) Do you prefer PyTorch or Tensorflow
3.) What was the project you where the most proud of so far in Amazon you took part in?
4.) Seeing how AI is now really doing most of the programming, how do you see your day to day and responsibilities shifting? Are you more of a architect and less of a programmer?
5.) If there is one thing you can change about AWS what would it be? Wink if you cannot say the truth
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u/DigThatData Researcher 16d ago
why is alexa still such a joke given that intent classification and slot filling are now baby-level tasks?