Hey everyone

After clearing the phone screen round, I got a call regarding the Applied Scientist virtual onsite round at Amazon.

It will probably be a 5 hour onsite (details are yet to be discussed with the recruiter). This sub has extensive information about the leetcode style questions but I wanted to ask the MLEs, Applied Scientist and Data Scientists on this sub as to what to expect in the ML depth and breadth round and Business application round. And how to prepare for each of these rounds? If you could share your some resources that would be helpful.

Also below are my leetcode stats, from here on I will focus mainly on Amazon but any other suggestions are appreciated.

This is a follow-up to my earlier post (LINK). I recently went through 7 interview rounds—2 phone screens and 5 onsite rounds—for an Applied Scientist 2 position.

The phone screens focused on machine learning (ML) fundamentals, statistics, probability, and a few basic data structures and algorithms (DSA) questions (though I don't recall the exact ones). The 5 onsite rounds were as follows:

1. ML Breadth Round: Covered a wide range of ML topics with a heavy emphasis on math.

2. ML Depth Round: A deep dive into the specifics of my resume and past projects.

3. Business Problem Round: I was asked to design Alexa from scratch—not the software system design, but the ML system design. This included identifying necessary datasets, tasks to be performed, model selection and justification, and evaluation metrics.

4. Behavioral Round (1.5 hours): A rigorous behavioral interview focused on leadership principles.

5. DSA Round: Two questions were asked—one similar to the course schedule problem, which required topological sorting, and the other was about finding the longest duplicate substring in a given string.

Although I wasn't offered the L5 (Applied Scientist 2) role due to my relatively limited industry experience, I did receive an L4 (Applied Scientist 1) offer, and it was at the top end of the L4 salary band. My next goal is to work hard and earn that L5 promotion next year.

For context, here's a snapshot of my LeetCode journey so far:

Hello everyone,

I want to provide my experience with Amazon Applied Scientist interview. I took a lot from this subreddit and similar communities and want to give back. I hope this will help some folks, especially those with academic background. I got an offer for L4 (Applied Scientist I) at the end of the process.

My background is that I obtained PhD in a non-ML field a year prior and then worked for a e-commerce company as an ML scientist before getting laid off. I have therefore ~4 years of academic experience and ~7 month of industry experience.

I start with the interview structure first, and then share how I prepared for technical and behavioural part. I will not share exact questions for obvious reasons, but everything was very similar to what you find online (on reddit or especially glassdoor).

Part one: interview

Phone screen (1hr):

• quick talk about a favourite ML paper (10-15 mins).

• ML coding question: implement an optimisation algorithm from scratch in Python (~20 mins).

• 3 LP (Leadership principles) questions, to one of which I did not answer.

Here I make a little note that I justified that I don't have a good story this one question. I read somewhere that it's better to not give an answer rather than give some trivial (or 'Bar-lowering') example. However, Later in the onsite prep-call with the recruiter I asked if its is OK to NOT give an answer, and she told that its better to at least say something. So it's still not clear for me what would the best tactics be. Don't put 100% trust into internet advice (including this post!).

Got positive phone-screen outcome email three hours after the end of the interview.

Prep call with a recruited (45 min):

Definitely very useful, take it if you can. It will give you a broader overview of topics in each part. You can find applied science topics on the internet, but prep call gives you a bit more information and expectations.

Virtual onsite (five 1h interviews, 15-60min breaks in between):

all loop interviews were more than 50% behavioural (LP questions) - keep this in mind. I'm talking about first 30-40 mins of each interview be about LP.

1st round (ML breadth):

• 5 LP questions.

• ML breadth questions about linear regression, KNN, types of supervision and so on.

Note after the first round: usually it is expected that each interviewer will ask 1-2 LP questions to test some principles. Here got 5 and it was obvious that they did not collect evidence from stories I told. It worried and demoralised me very much and I thought I failed this round. On top of that some of my ML answers were not complete... Lesson I learned here is to not be discouraged if one interview (even the first one) goes not ideally. I performed much better on the later loop interviews.

2st round (Bar Raiser):

• 3 LP questions

The bar raiser was very positive and supportive, which helped me to overcome discouragement after the first round. LP question were discussed very deeply, with follow-ups on both behavioural part (e.g. impact) and technical part (how I interpret why model performed better compared to baseline). Very pleasant round and I think I nailed it.

An example of a non-trivial BQ (you can find it even online): time when I delivered something for customer that liked, but they did not knew they needed it.

3rd round (Coding):

• 3 LP questions

• Programming question

This was the hiring manger interview. Coding question was not leetcode-style, it was a string manipulation question which is solved with one for loop and a couple of if-else statements. Here one, as usual, thinks out loud and consider assumptions and edge cases. Eventually I was asked to implement the solution for the exact question I was given and do not try to make it more extendable or generally applicable. Here I got a bit confused by the logic and code was not super-readable, but we did not have time to adjust it.

Additional 15 minutes (on top of 1h interview) HM explained the role and answered my questions. Good round, but my programming could have been better.

4th round (ML breadth?):

• 2 LP questions

• ML topics

Here I expected to be the ML-depth interview (when I am asked about my projects), but the LP questions smoothly transitioned into ML breadth discussion. I was asked about NLP and then about tree-based ensemble methods. Since I worked with ensemble methods before, we did a deeper dive into how training it performed, what are the industry standards and so on. Round went really good.

5th round (Science application round / miniature system design):

• 4 LP questions.

• ML research problem related to the role

On the last LP question, I had to repeat the story I gave during the bar-raiser. But obviously I tried to adjust the story towards the particular question which was different from the bar-raiser question. Surely during the debrief they should have noticed that, but I could not come up with another example.

Science application part is to design a system relevant to the role, but with more general discussion (e.g. start with number of users, ask if there is a system in place which already produces output and log data, if not, how to build data-collection system and so on, batch vs real-time processing, A/B test). Definitely here I made some mistakes like not asking some important clarification questions but overall I did a good job. Without preparation, I would not have passes this technical question. Formally this is NOT ML system design, but just a science case study.

Phew... that was very intense and draining - be ready for that. You may opt to split the loop in two days.

On the fourth day after the loop I got an email with subject 'amazon outcome' and was invited to schedule a call. We scheduled it next day and I got a verbal offer, asked for starting date and salary expectations. Waiting for the outcome is mentally very tough, be prepared for that.

Part two: some preparation tips

Coding:

By the time of the onsite, I had around 120 leetcode problems solved. In the last weeks I focused on the Amazon-tagged problems of easy and medium difficulty with arrays, strings, two-pointers and other not-so-advanced algorithms. Honestly coding task I was given on the onsite is not leetcode-style at all.

ML breadth:

Skim the list of topics recruiter will sent you. You are not expected to know everything, it's OK to not know about some niche subjects. But I believe that knowing about popular themes (e.g. Transformers) is essential even if you go to Fraud detection team.

ML systems:

Due to the lack of time I studied ML design only for systems relevant to the role. Recruiter told beforehand that design task is very likely to be about the team's job. This task is about thinking about customer experience.

ML depth:

You need to be ready to go into detail of your work. So if you published a paper three years ago and don't remember much, better to re-read it and think about decisions you had to make to chose one approach over another.

Leadership Principles:

Here I will elaborate, since a lot of people asked in DM about how I prepare these. It will be relevant for all roles of L4-5 levels. For me, the largest obstacle is mapping Amazon's principles to stories from my PhD. Due to the limited experience in industry, out of my ~20 stories only 5 are from industry (+story from my industry hackathon experience).

Most important prep tip for LP: story bank.

I prepared my story bank with the help of AI. Create stories using STAR format, paste it to ChatGPT and ask to format it towards Amazon LP in a more concise way. Prompt it with the role and level you are interviewing for. Don't forget to include metrics of success whenever possible. Make as much non-trivial stories as possible. Obviously check ChatGPT answers, as it tends to replace/omit details. After you have created stories (I made a bit more than 20 stories), save them In a pdf, feed this pdf to ChatGPT and ask to create a table with a list of stories and LP it covers (usually story covers 2-3 LPs). Find which LPs are strongly present and which are week/absent. Note that you will not be asked fours LP out of 16 total. Then iterate: either add stories or adjust some stories to fit more LPs. Hardest part for me were stories about tight deadlines, conflicts and customer impact.

Don't overrely on ChatGPT: I mostly tried to map my academic language into something an Amazonian would like to hear, and emphasise impact.

For academics: customer obsession works in science too! For example, your customers are your fellow researchers which will use your papers in future. How to do you think about those people when writing a paper? May be you open-source your datasets and code for the ease of reproduction? Or may be you help your co-author with refining selection criteria to reduce false positive in the paper's catalogue? All those are examples of several LPs.

On using notes: you can and should use notes during the LP questions. I prepared my list of stories as collapsable sections in Notion and just unfold it once I see the story fits the question. You may take a few seconds to skim the story and notice key points (highlighted in bold). Once you start talking, you may reference your notes but obviously do not read from the screen (you will loose fluency and it will not sound natural). Couple of times I told interviewers that I want to have a minute to think about the question and select a story from my list. It was completely OK.

Good luck!

Hi Everyone

Hope all of you are doing great.

This is an extension of this post -- https://www.reddit.com/r/MachineLearning/comments/1p3omq2/d_amazon_applied_scientist_i_interview/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button

I had my phone screen, and it went like this --

1. No LP Questions

2. All questions were directly towards my research works, and then diving deep into all the techniques and architectures of deep learning

3. Machine learning questions on SVM, Random Forest, PCA, Some questions on PAC learning.

Two hours after the interview, I received an email from a recruiter stating that I will be moving forward to an interview loop consisting of five 1-hour interviews. Now that the recruiter is from Singapore, as I can see (mainly that the team is based in Singapore).

Now, guys, please share your interview experience or any tips. (bit scared on what will be asked n all )

My background --

1. Master's in AI from a top IIT

2. 3 A* publications

3. Research internship at a top research company.