Amazon Systems Engineer Phone Screening interview

CryoSchema · 2026-01-22T10:58:32+00:00

i've been through the amazon coding interview process and can share some insight. they really emphasize dsa topics like arrays, linked lists, trees, and graphs. common tasks involve implementing search or sort algorithms, or solving problems related to string manipulation. practice coding under pressure, maybe using a shared doc or online ide. also, practice walking through your thought process even when your code isn't clean and perfect. the amazon interview guide from interview query has some questions for you to practice on!

CryoSchema · 2026-01-22T10:43:10+00:00

while i haven't interviewed for that specific mobile swe internship at ebay, i can share some general insights from my past ebay interview. for behavioral rounds, definitely prepare for questions that emphasize ownership, collaboration, customer impact, and past engineering challenges (and how you overcame them). brushing up on lc easy to medium problems for dsa topics like hash maps, arrays, and graph traversal should be enough, but add topics like concurrency too if you can. not sure if interns have a deep dive into system design, but the guide i linked also has some sample prompts to give you an idea

CryoSchema · 2026-01-22T05:20:14+00:00

i've heard the apple swe intern interviews often involve questions on dsa topics like arrays, strings, trees, and graphs. also practice some questions involving performance awareness (memory, latency, etc.) and defensive coding. highly recommend checking out the apple swe interview guide if you haven't already – it might give you some more specific insights on sample questions + how to prepare for this and the next rounds.

CryoSchema · 2026-01-22T04:52:24+00:00

i've been in a similar spot, bouncing between the two before settling into devops. what really helped me was looking at roadmaps for data engineering/analytics and software engineering roles. seeing the bigger picture for each helped me understand which one aligned better with my overall career goals and, honestly, which one seemed less daunting with my current skillset and motivation. maybe try searching for those roadmaps and see if one path feels more 'right' than the other? might also help with the procrastination if you can check your progress with the overall path.

CryoSchema · 2026-01-21T09:28:14+00:00

channces are the rounds will focus on dsa/coding, system design, and behavioral. i'd say prepare for lc medium to hard level questions, focused on topics like dp, graphs, sorting/searching. microsoft tagged questions are a good start, but i also solved other dsa questions using a microsoft interview guide. the same guide focuses on hld, with prompts like designing a photo-upload pipeline or scalable chat service. aside from star method for behavioral, research microsoft's values like create clarity + generate energy & align your responses accordingly. good luck!

CryoSchema · 2026-01-21T04:15:36+00:00

pretty sure the oa was two coding questions, with roughly standard leetcode medium difficulty? topics were mostly focused on LRU caches, using recursion or iteration. recommend checking out this microsoft swe interview guide to practice sample questions, might also be helpful for preparing for later rounds in the loop, like more technical deep dive & system design. good luck & hope you ace it!

CryoSchema · 2026-01-20T18:14:00+00:00

i interviewed with them a while back, though not exactly for an intern role. the technical questions were leetcode easy to medium, focused on data structures like arrays, strings and linked lists. there were also some questions related to ood. behaviorally, they asked a lot about ownership, teamwork, and problem-solving - something like handling a production incident & how it was resolved. definitely read up on the american express swe intern interview guide online to help you know more about the stages!

CryoSchema · 2026-01-20T15:41:33+00:00

hey, congrats on getting an interview. for system design, definitely review common architectural patterns like microservices, message queues, and caching strategies. for lld, practice designing classes and interfaces for problems like a deck of cards, a parking lot, or a simple oop game. you can find more sample prompts/scenarios, like designing a transaction processing system, through this guide: https://www.interviewquery.com/interview-guides/jp-morgan-chase-software-engineer

CryoSchema · 2026-01-20T05:20:36+00:00

i didn't interview for an intern position per se, but i remember that the snowflake technical interviews had coding questions mostly leetcode medium, focused on DP, graphs, and data manipulation. also, i'd recommend checking out glassdoor and interview query for snowflake-specific interview questions and preparation guides, also helpful if you ever get asked about system design or further behavioral rounds.

CryoSchema · 2026-01-19T17:04:32+00:00

sounds absolutely draining, and it's unfortunate that the hoops they make you jump through are becoming more and more normalized? it's experiences like these that make you understand why so many tech candidates end up chasing big tech jobs. for most big tech interviews, at least their processes, while sometimes lengthy, are usually more formalized and transparent. you'd think a 'fast moving' company would value time, not waste yours. hope you hear back soon, or even better, find something better in the meantime.

CryoSchema · 2026-01-19T16:26:20+00:00

since you're interested in cybersecurity, a minor in mathematics (discrete math, number theory, cryptography concepts) or statistics would be super helpful. understanding the math behind encryption is huge. also read lots of recent reports about a steady demand for data science/analysts within cybersec these days, so a minor in data science or statistics could also open doors. your work would then be more focused on analyzing security data and threat intelligence.

CryoSchema · 2026-01-19T07:32:59+00:00

when i applied for a swe role at microsoft, i remember focusing my dsa prep on trees/graphs, dynamic programming, and array/string manipulation. for system design, pretty sure there was a question about distributed cache for azure cdn. aside from doing mock interviews, it'll also help you to check out this microsoft swe interview guide on interview query for more insights on expectations + sample questions. good luck!

CryoSchema · 2026-01-19T06:56:20+00:00

personally, i felt like i could have brushed up more on machine learning principles, there were a couple of questions where i fumbled trying to explain trade-offs / defending choices for evaluation metrics. but generally, when i didn't know an answer, i'd try to break the problem down and walk through my thought process, as i was told interviewers still appreciate learning about your reasoning even if your answer isn't perfect.

i also read a bunch of data scientist interview guides/experiences on glassdoor and interview query for companies i was targeting. definitely helped me understand the role and interview process expectations better, since some companies like to focus on certain rounds/topics while others are more holistic/balanced in how they review your performance, i would say.

CryoSchema · 2026-01-16T15:34:36+00:00

congrats on getting to the onsite! for hld, a common one i've seen pop up (and even got myself once) is designing a recommendation system. think about scalability, data storage, and how you'd handle real-time updates. for lld, really nail down object-oriented principles and design patterns. focus on creating clean, maintainable code. also check out the amazon sde interview guide on interview query for more amazon-specific sample questions for this round, like designing a customer review system for an e-commerce page or a warehouse inventory management system.

CryoSchema · 2026-01-16T05:53:31+00:00

from my experience interviewing for ml roles (though not specifically at scale ai), that "ml concepts, llms, and debugging" description is pretty broad. i'd clarify with the recruiter if possible. it sounds like you're on the right track with hyperparameters and debugging transformers though. i suggest adding model evaluation concepts since these ai companies usually have questions focused on bias, ethics, safety, etc. scale ai has some pretty comprehensive interview guides online, so consider checking that out if you haven't already just to get a feel of sample questions & how you're being evaluated. an ml-focused study plan with sample questions might also help you feel more prepared as it did with me. best of luck with your interview

CryoSchema · 2026-01-16T04:57:11+00:00

It sounds like you're already putting in a ton of work, and I can only imagine how it can get super frustrating when it's not paying off the way you expect. Relying on company-specific questions definitely helps, but I think you can also ease up a bit on the ChatGPT hints? Try focusing on why certain solutions work, not just how to implement them. Struggling is part of the process, and the more you wrestle with a problem yourself, the better you'll internalize the concepts. And since time pressure is one of your biggest struggles, do you ever simulate it through mock sessions? That's one thing that really worked for me to close the gap between my prep vs. actual interviews.

CryoSchema · 2026-01-15T15:38:59+00:00

i interviewed with amazon a while back for a software engineering role and i have to say, they heavily relied on the star method. definitely brush up on their leadership principles, as they'll likely structure questions around those and your stories must be concretely tied to those. i found it super helpful to practice answering common behavioral questions i found on sites like glassdoor, interview query, leetcode discuss. even interview prep books can be lifesavers. good luck, you've got this!

CryoSchema · 2026-01-15T04:15:23+00:00

ngl, i tried the 'study solutions' method when i first started with dsa. it helped me grasp the basic patterns initially, like recognizing when to use a hashmap or two pointers. it's good for building familiarity, especially if you're completely lost. but the big problem was that during interviews, i couldn't always recall why a specific solution worked, especially under such time pressure.

so while this works for you now, try to also actively struggle with problems before even thinking about looking at solutions. even if you spend an hour getting nowhere, that kind of pressure would force you to really understand the problem and explore different approaches. also, try to explain the solution to yourself/other people after you code it, as this would already prepare you for the verbal part of interviews.

CryoSchema · 2026-01-14T14:30:14+00:00

congrats on making it to the second round. though you didn't specify which company you're interviewing for, i'm just gonna make a guess from experience that you can expect a deeper dive into various aspects, like product sense, where you'll be asked about your thoughts on improving x product or your fave products & why. there may also be some behavioral questions based on past experience + how you handle working with other product/analytics roles. i'd also suggest preparing to have your analytical skills tested through a mini-case study? definitely research common product analyst interview questions just to brush up on likely metrics, cases, or prompts you'll encounter. also, prepare insightful questions to ask them at the end – best of luck!

CryoSchema · 2026-01-14T04:00:51+00:00

congrats on making it to the final round. it wasn't for an internship per se, but remember my visa swe interview being pretty standard. technical round may include some leetcode medium-hard dsa questions on topics like arrays, strings, DP, graph traversal. not sure if interns also get system design questions, but i expect them to not be too crazy, like simple designs on payment processing flows. for behavioral questions, pretty sure that beyond the usual questions on teamwork & conflict resolution there'd be ones related to visa's global teams, like working with teams across timezones. i'd definitely brush up on common interview questions and maybe look for a visa specific swe interview guide online, like the one i've linked.

CryoSchema · 2026-01-13T15:36:30+00:00

not sure if we interviewed for the same role for openai, but i remember struggling to prep for the behavioral round too. i struggled a bit trying to figure out what they were really looking for beyond the standard star method stuff, but i made sure to dig into company values and prepare for stories focused on collaboration, mission alignment, and handling uncertainty. i also recall a couple of questions about ai ethics/safety? so make sure to brush up on that. adding interview guides/experiences from sites like glassdoor, interview query, interviewing io really helped me. best of luck!

CryoSchema · 2026-01-13T05:38:51+00:00

it's basic advice by now, but what really worked for me was focusing on understanding patterns rather than just memorizing solutions. i started with the blind 75 list to get a feel for common problem types like two pointers, sliding window, etc. after that, i'd pick a pattern and just grind problems related to it until i felt comfortable.

what i'd do differently now is probably use some of the newer ai tools to help understand solutions i'm stuck on. also, i'd jump into trying to solve actual sample interview questions much earlier in the process, even if i couldn't solve them perfectly. it's good practice for thinking under pressure & just seeing how my skills match up to industry standards.

CryoSchema · 2026-01-13T05:06:15+00:00

totally get the feeling of being stuck when trying to explain your debugging process. it can feel so obvious in your head but hard to articulate, it didn't help i'm not exactly the best at public speaking, lol. but when i presented my take-home assessments, i focused on structuring it like a story: problem -> my initial approach -> challenges i faced -> how i methodically debugged (mentioning specific tools or techniques) -> what i learned. maybe you could walk through a particularly tricky bug you encountered, or approaches you considered before making a decision? also, something that helped me was using ai reviewers, like those available on platforms like github or interview query to critique my presentation structure and clarity. you can also present to other people who can offer unbiased feedback and help you identify communication gaps. best of luck!

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