Amazon
Amazon has a lot of confusing role titles so let's go through them together.
Applied Scientist
In short, Amazon Applied Scientists are Researchers and SWEs.
You need to have the full abilities of a Research Scientist (almost everyone has a PhD) but also be capable of writing code and building solutions to your research. This is the highest paid position at Amazon and generally the most coveted.
However, we would encourage you not to think of this as a traditional 'applied' role where the focus is often more internal research and code implementation. Applied Scientists have a large focus on publishing and working with the research community. It truly is a Research Scientist role except that you must also be capable of being a SWE as well when needed.
Both Applied Scientist managers and Applied Scientists are paid higher than their Research Scientist or SWE counterparts.
You can expect these to be some of the most challenging interviews at Amazon. You will have a set of research interviews where the interviewer will ask ML fundamentals as well as deep dive into your past research. Additionally, you should expect at least 1 Leetcode-style SWE interview.
Research Scientist
This is a pure research role. You are generally publishing and researching an area specific to your background and the team you’re on. You can expect all of the interviews for Amazon Research Scientist positions to be about your past research, your future research ambitions, and ML fundamentals. Expect the interviews to go deep on your area of expertise. Almost everyone who is an RS at Amazon has a PhD in Computer Science or Applied Mathematics.
Sometimes the interviewers will throw in a Leetcode question or two. Don't panic. They are just checking to see if you could alternatively be an Applied Scientist. We know people who have fallen flat here and still gotten excellent Research Scientist offers so don't let it scare you!
Data Scientist
Data science at Amazon is rarely about research. In this role you can expect to be doing DS work that moves Amazon's products forward. Amazon Data Scientists do really awesome work but it's not research.
Most DSs at Amazon do not have PhDs. If Applied Science and Research Scientist are like non-identical twins, DS is a distant cousin.
Research Scientist or Applied Scientist?
Aren't sure whether you should interview as a Research Scientist or an Applied Scientist? If you feel like you can solve Leetcode medium (or ideally hard) questions and enjoy coding then we'd recommend applying for AS. If you hate coding or the idea of Leetcode is worse than eating an entire raw lemon then go for RS.
Recruiters at Amazon are often siloed (i.e. they recruit for specific teams, not the full organization) so it's not uncommon for recruiters to try and force you into the AS or RS bucket based more on their hiring recs than your abilities. We recommend advocating to interview for the role that best fits your skillset and asking the recruiter to introduce you to those teams or recruiters who are responsible for recruiting for those teams.
All Amazon interviews will include a bar raiser interview so if one of your interviewers is more senior or is being more challenging, that's normal.
Netflix
Netflix Research, although filled with many smart, hard working scientists, rarely publishes because they focus more on using research internally.
Netflix Research Scientist interviews are comprehensive. You can expect a recruiter screen, several interviews on ML including problem framing and fundamentals, a coding screen, and generally a few interviews that are specific to your team (i.e. going deep on a specific research area). Additionally, expect conversations with the hiring manager to make sure you are a culture fit - culture is extremely important to Netflix so we recommend reading their guide to company culture beforehand.
Research Scientist
The prized research role at Google is the Research Scientist role.
From a compensation perspective, they are paid better than any other role at Google and generally you are focused on producing and publishing groundbreaking research. Google historically has a significant preference for their Research Scientists to have a PhD and publish papers at top conferences.
The bar is high but generally people are humble and thoughtful. Being a researcher at Google is one of our favorite roles at the company because the community is strong and small - similar to what it was like to be a software engineer at Google ~10 years ago or what it's like to be part of the Principal Engineering community at Amazon.
The team you join will have a large impact on your interview experience. We'd recommend interviewing with teams that align well with your past research and expect them to dive into it. Most Research Scientists at Google are expected to know how to code so you can generally expect two traditional Leetcode-style coding questions. The difficulty of those questions vary by team.
If you have an engineering-heavy background (which is rare for this role) vs. a research-heavy background, expect the Leetcode problems to be hard - you will need to shine on your engineering (and still be strong in research).
As with all interviews at Google you will have a Googly-ness interview to determine if you are a culture fit - just be nice, come with a few genuine questions, and have a good time. The easiest way to fail the Googly-ness interview is by not being a nice human - this interview isn't about your technical abilities so you don't need to brag/boast or try to be outstanding.
Research Engineer
While they’re not common, you also may come across a job description for a Research Engineer position at Google.
At Google Brain, Research Engineers have traditionally been allowed to flex between writing code and focusing on research. In contrast, at DeepMind and other parts of Google they’ve been focused more on ML engineering.
Research Engineers typically have a Masters or PhD.
It's unclear as Google merges their divisions exactly how this role will be impacted. We'd highly recommend talking to current Research Engineers on the team you’re interviewing for to get a sense on what their % time split is between engineering and research so you can make sure this role is a fit for your interests and ask to interview for a Research Scientist position if not.
Data Scientist
Similar to Amazon and Facebook, Google Data Scientists are a distinct role from researchers. They generally support product development instead of publishing research and have a mix of educational backgrounds from Bachelors to PhDs but a PhD is rarely a hard requirement.
At a time when everyone in the industry is moving from open to closed source we love that Facebook Artificial Intelligence Research (FAIR) continues to publish creative, groundbreaking work.
Yann LeCun is a brilliant but controversial leader and we personally like that he hasn't accepted that LLMs in their current transformer architecture are the be-all-end-all. Regardless of whether he is right or wrong we appreciate having different views in the community.
It's worth distinguishing two confusing AI title differences at Facebook. There are Research Scientists within Facebook and then there are Research Scientists at FAIR.
FAIR Research Scientists focus on producing groundbreaking research and publishing papers - they are the equivalent of Research Scientists at Google.
Researchers at FAIR are given a lot of autonomy on what they want to work on. It's an excellent place to produce and publish in today's climate. In contrast, the work that a Research Scientist does outside of FAIR (but at Meta) varies widely. If you have a PhD and are doing engineering work you are often called a Research Scientist even though the work may not be research-focused at all. However, some RSs (outside of FAIR) do applied research for Facebook products.
Interviewing at FAIR
Assuming you want to be in a publishing role, here’s more information on interviewing with FAIR:
The on-site is similar to a faculty interview at an academic lab where you have a 'job talk' and several research-focused rounds. However, you can also expect to have multiple rounds of coding interviews which you need to pass. Sometimes a research and coding round will be merged into an 'in-domain' interview where they ask coding questions or more general questions that are related to your research domain.
A final note on Meta - if you are interested in coding and machine learning infrastructure then a ML Engineer role is probably the right fit for you. It's very heavy on coding, pipelines, and systems but you are often working alongside a research team and supporting them so having some understanding of research is still valued.
MLE interviews are very similar to SWE interviews but you can also expect to cover ML fundamentals.
Conclusion
The demand for AI researchers continues to grow, irrespective of layoffs and other business shifts. When we talk to leaders at FAANG and late stage startups there is immense pressure to incorporate AI (especially generative AI) into their products and that pressure comes from the board and investors. We don't expect demand for AI researchers to slow anytime soon.
We've reached all-time highs in our negotiations for AI researchers in recent months at Facebook, Amazon, Google and Netflix (here are 2023 benchmarks for researcher roles) despite some companies attempting to try and be stingier given the market.
However, research roles are changing in big tech as AI research becomes more closed source.
We'd recommend interviewing at Facebook, Google, Amazon, and Netflix as even just being in the interview process with these companies can help you negotiate exceptional offers. You'll likely need to spend some time brushing up on your Leetcode and we'd also highly encourage you to prep answers for questions on your research and to read the latest, cutting-edge papers in your field.
We've had 100s of clients join top AI labs so if you have questions about the interview process, what it's like to work at each lab, how to pick a team, or how to negotiate - just reach out to hi@teamrora.com for guidance :)
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