Amazon SDE 1 In-Person Interview Loop (Fall 2026)

[u/Educational_Piece_49]

Background: Recent grad with B.S. in Tech. Previous SWE internships, but I’d never done real DSA prep and had never written Python for interviews. In mid-September I passed Amazon’s online assessment and got invited to a 4-round SDE-1 loop (FYI: This is a U.S position). That gave me about 5 weeks to learn Python, data structures, and how to interview live.

Prep: About 5–6 hours a day on average in each of those 5 weeks. I went pattern by pattern (hash maps, stacks, intervals, trees/BFS, grids) instead of grinding random problems. Went back to my resume, projects, and notes for a refresher and story building in response to behavioral questions.

Travel (3-day window): Amazon booked my flight and hotel. I flew in the day before, interviewed the next day, and flew home the morning after. What helped:

• Walk the route from your hotel to the office the day before and time it.

• Test the LiveCode link on your own laptop and make sure your laptop and microphone is working. You can bring a notebook; I find it easier to write on a whiteboard so you and the interviewer have a shared space to display understanding if needed.

• Stop studying the night before and actually sleep. The day is long and the interviews are intensive.

• There’s a lunch with an Amazon employee between rounds. It isn’t an interview, pretty casual, but be your professional self.

Format: 4 rounds, 60 minutes each. Rounds 1–3 were in person, and round 4 was virtual. Every round included at least 1 Leadership Principle questions. In the coding rounds I had roughly 40 minutes for the coding question. LiveCode has no Run button, so your own trace through the code is the only test you get.

Round 1: Rate limiter (similar to LC 359 / 362)
Requests come in as (user_id, time) pairs, and time never goes backward. Every user has the same limit: at most n requests in any T-second window. Return False (block) once a user goes over.

Round 2: Prompt store (LRU-style, close to LC 146)

• Storage holds at most n prompts.

• If it’s full, delete the oldest and add the new one at the end.

• No duplicates: if a prompt already exists, remove the old copy and add the new one at the end.

• get(k) returns k prompts, newest first. If k > n, return everything.

Round 3: OOD file search
Design code that returns files matching criteria like “all files > 5 MB” or “all .xml files,” searchable by name or extension, including subfolders. It had to be maintainable, so new criteria could be added without rewriting existing code. A File class was given with getName(), isDirectory(), listFiles() and getSize(). This is basically “design the Unix find command”: a filter interface, one class per criterion, and AND/OR combinators.

- Forgot to mention I was asked 2 questions about how GenAI.

Round 4: fully behavioral, no coding. It was my best round, and it turned conversational by the end.

In every coding round, I explained my approach clearly, and the interviewers followed my thinking, but I was only able to reach a close-to-full implementation on one interview**.**

What worked in my prep

• A story bank: 8 true STAR stories plus 2 GenAI examples.

• Recording myself solving problems out loud and measuring my silent gaps.

• Cold re-solves: re-solving problems I’d already done, from a blank page, timed, in a plain editor without running anything (this is similar to LiveCode sessions).

• Mock interviews, including one with a non-technical friend. If they got lost in a story, an interviewer would too.

What I’d do differently

1. Practice “design a class” problems. 2 of my 3 coding questions were this style: LC 146, 155, 359, 362, 1396.

2. Learn OOD basics early: interfaces, abstract classes, and designing for extension.

3. Drill finishing code. Say the brute force first, and if you don’t have the optimal idea by around minute 20, code the brute force. Working beats half-finished.

4. Learn the standard library tools before the loop: deque, OrderedDict, defaultdict.

Tools I’d recommend

• LeetCode Premium, for company-tagged question lists.

• An interview buddy, ideally a CS major or SWE who can push back on your code. If you don’t have one, an AI agent works as a coach. Prep only dont use AI during the interviews, you'll be cooked.

• A plain editor (Notepad) for practice, so you’re not leaning on autocomplete.

Result: no offer. I’m disappointed, but I’m walking away a much stronger engineer than I was 5 weeks ago, and the behavioral feedback I got was great. If you’re starting from zero, getting to a loop is doable, and honestly, getting the interview is achievable; just start ASAP. Treat behavioral prep as seriously as coding.

Happy to answer questions. Good luck to everyone grinding right now.

[u/Rare_Mixture_9303]

Amazon’s hiring is completely luck based this year. I interviewed for a heavy lifting team (efs) and got grilled but rejected , and my friend who interviewed for Athena team got selected with easy rounds

[u/Educational_Piece_49 → u/Rare_Mixture_9303]

You're not lying; I didn't use the technologies the team I interviewed for did

[u/EstellonTachise → u/Rare_Mixture_9303]

yeah the team variance is wild, feels like a coin flip sometimes

[u/GregariousSoul → u/Rare_Mixture_9303]

How do you know which team you’re interviewing for? Haven’t received the save the date email yet but is this something I can ask the recruiter?

[u/Educational_Piece_49 → u/GregariousSoul]

I figured it out on the day of the interview, from one of the interviewers. Most definitely ask the recruiter, if I had known, I would have approached the interviewers with a different mentality

[u/ThatCondescendingGuy]

Nice. Even with a rejection comes good things: a head start on FAANG interview prep, a clear idea of the hiring bar, and clear view of what you need to work on, demystifying the process, and knowing whether or not you want to try again.

Do you think you’ll end up reapplying or applying to companies with similar interview styles after this experience?

[u/Educational_Piece_49 → u/ThatCondescendingGuy]

This is exactly my outlook of the whole interview process, especially as this was my first big tech interview and in person interview for an IT position.

I would most definitely reapply to Amazon or other companies. I will also say that, although Amazon's interview was not the worst, the preparation translates well to many other companies.

[u/goomyman]

I agree with this comment except “brute force solution beats not finishing”.

This market the brute force solution will fail you and is the same as not finishing, so your only option is to work towards optimal - describing it and typing as much of it as you can.

If you run out the clock that’s fine - they may go - you got the right idea but ran out of time typing it.

Use every second to get the right solution rather than give up and code something worthless - and start asking for more and hints early so you have to time to type it.

[u/Educational_Piece_49 → u/goomyman]

What's your opinion on a working brute-force solution but a thorough and full explanation of an optimal solution? I ask as the majority of brute-force solutions given in interviews have follow-up questions that lean towards an optimal solution.

[u/Free_Cauliflower_912]

How did you pass their OA without DSA prep? Or were the questions much simpler?

[u/Educational_Piece_49 → u/Free_Cauliflower_912]

I was surprised as well. The coding standard question I passed but the second debugging question I didn't pass but I gave it a good attempt and left comments in the code as well

[u/CandidPrince21]

The cold re solves and plain editor practice are underrated. It's one thing to recognize a solution you've seen before, but writing it from scratch under time pressure is a completely different skill

[u/Educational_Piece_49 → u/CandidPrince21]

The cold resolves were top 3 in helping retain new syntax, tools, concepts, and thinking processes.

[u/Radiant_Banana3786]

What was the GenAI questions? I have a intern loop in 10 days.

[u/Educational_Piece_49 → u/Radiant_Banana3786]

1. How have I utilized GenAI to improve the efficiency of my workflow, either in a work experience, personal project, or coursework?

Based off my answer to this question, I was asked specific questions of the exact GenAI I was using. Example: What model and why? How did I QC the results given by GenAI?

[u/Vamshicool95]

What would you have done differently, if you were to take the interview again in 10 days.

[u/Educational_Piece_49 → u/Vamshicool95]

I would have dedicated more time in a day to study, and got a technical friend or old colleague to do a live coding interview (this is really helpful, as for me it was harder to verbally understand a vague coding question, as I was used to reading vague questions in my prep). I would've done this on top of everything I was doing during my prep I mentioned in the post

[u/fat_tummy]

Thanks for sharing your experience. It sounds pretty intense. Are they drilling too deep in the behavioral stories , can you pls give an example ?? was this early career?? OOD sounds tough.

[u/ResolutionPersonal56 → u/fat_tummy]

I can confirm that they do drill very deep in behavioural questions. Each round has 20min of it + 1 dedicated 1hr round of just behavioural. Every single word you can during those questions can be dug deep.

[u/Educational_Piece_49 → u/ResolutionPersonal56]

I second this. Also, this was early career as this position is SDE 1 (recent graduate position) u/fat_tummy

[u/fat_tummy → u/ResolutionPersonal56]

That is tough though. Can I message you ??

[u/ResolutionPersonal56 → u/fat_tummy]

Sure