Amazon interview notes, page 98 of 159

FAANG / Big Tech

Most of these Amazon write-ups come from Software Engineers (1,092), then Data Scientists (182) and Product Managers (165). The most reported rounds are the technical phone screen (618), the behavioral / leadership round (309), the online assessment (228) and the system design round (159).

1581 interview notes · updated Oct 2026

Amazon·Software Engineer·Technical Phone Screen

Jun 2026

Amazon technical screen for a software engineer role, heavy on ML fundamentals. Four meaty topics back to back, felt more like a grad school oral exam than a typical coding round. Walked out unsure if I'd done well or completely bombed it.

Compare L1 and L2 regularization. How do they differ in terms of sparsity, the geometry of their optimization landscapes, and how they handle outliers in the data?
How do you choose between loss functions like MSE, MAE, logistic loss, and cross-entropy for different regression and classification problems? What are the gradient properties you care about?
Walk me through PCA: what is it actually optimizing, how do you fit it and then apply it to new data, and how do you decide how many components to keep?
Explain how Random Forests are built, their bias-variance trade-off, the limitations of impurity-based feature importance, and which hyperparameters matter most.

“I knew the sparsity answer cold (L1 pushes weights to zero, L2 shrinks them evenly) but fumbled when they pushed on the geometry angle.”

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Amazon·Software Engineer·Online Assessment (OA)

Jun 2026

Amazon SWE online assessment with a classic word break problem. Pretty standard stuff if you've done any backtracking before, but the output format tripped me up a bit.

Given a string and a dictionary of words, find all ways to insert spaces into the string so that every resulting word exists in the dictionary. Return all valid sentences.

“This is word break II basically.”

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Amazon·Software Engineer·Onsite - Coding / Algorithms

Jun 2026

Amazon SWE coding round with a multi-part Morse code problem that escalated pretty quickly from basic encoding into full-on backtracking with dictionary lookups. The problem was well-structured but part 4 definitely separated people who had actually thought about trie/memoization from those who hadn't.

Implement a Morse code encoder that converts an uppercase English string into Morse code, with letters separated by spaces.
Implement the reverse: decode a space-separated Morse string back into the original text.
Given a continuous Morse string with no separators and a word dictionary, find any single dictionary word whose concatenated Morse encoding matches the string exactly.
Given a continuous Morse string and a dictionary, find ALL possible sequences of dictionary words whose concatenated Morse encodings exactly reconstruct the full string.

“Straightforward warmup.”

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Amazon·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026

Amazon SWE behavioral round, pretty much just deadline and follow-up questions. Nothing crazy but the follow-up tripped me up more than I expected.

Tell me about a time you couldn't meet a deadline.
What did you do about it?

“I had a story ready but it felt too small for the room, like the stakes weren't high enough.”

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Amazon·Product Manager·Onsite - Behavioral / Leadership

Jun 2026

Behavioral screen for a PM role at Amazon. Just one question but it's the kind that can spiral if you're not careful with it.

Tell me about a time you had to handle a high-pressure situation.

“I went with a launch deadline story which felt right in the moment but looking back it was pretty generic.”

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Amazon·Research Scientist·Technical Phone Screen

Jun 2026

Concept-heavy technical screen at Amazon for an RS role, focused almost entirely on LoRA and its PEFT variants. The interviewer went pretty deep on the math, hyperparameter choices, and serving patterns, with a pivot toward RLHF interaction at the end. Felt more like a paper review session than a standard ML interview.

Walk me through LoRA mathematically. What exactly is being trained and what is frozen?
How do you choose rank r in practice, and what role does the alpha scaling factor play?
Compare QLoRA and DoRA. What does each one change, and when would you pick one over the other?
What is AdaLoRA and how does it differ from standard LoRA in terms of rank allocation?
When does LoRA underperform full fine-tuning? What are the failure modes?
How does LoRA interact with the multi-tenant serving pattern? How would you manage multiple adapters in production?
How do LoRA adapters behave during RLHF or PPO training? Are they frozen or updated?
Which target modules should you apply LoRA to, and how does that choice affect quality versus cost?

“This one I actually felt okay about.”

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Amazon·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026

Amazon SWE behavioral loop, three questions back to back all focused on leadership principles. Pretty standard but they pushed hard on trade-offs and numbers each time.

Tell me about a time you delivered something under a very tight deadline. Walk me through the trade-offs you made and what the outcome was.
Describe a situation where you went above and beyond what was expected of you. What did you do, and how did you measure the impact?
Give me an example of a time you convinced stakeholders who disagreed with your approach. How did you handle the pushback and what was the result?

“I had a decent story ready but fumbled the trade-off part.”

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Amazon·Product Manager·Onsite - Product Sense / Strategy

Jun 2026

Amazon product interview, one question about growing a new app's user base. Pretty sparse on details but the question itself is a solid strategy case.

How would you double a new app's user base within six months?

“This is the kind of question where you think you have an answer and then realize halfway through that you haven't actually said anything concrete yet.”

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Amazon·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026

Amazon behavioral round for a software engineer role, three questions back to back, all focused on how you handle pressure and whether you actually reflect on your work. Pretty standard but the third one tripped me up a bit.

Tell me about a time you ran into a significant obstacle. How did you handle it?
Describe a situation where you had multiple competing tasks and a tight deadline. What did you do?
Looking back at a past project or task, can you think of a better approach you could have taken? What would the tradeoffs have been?

“Had a story ready for this one and it came out okay.”

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Amazon·Software Engineer·Hiring Manager Screen

Jun 2026

Amazon SWE interview, not much to go on here. The only thing mentioned was a question about hiring practices, which felt more like a meta conversation than a real technical screen.

How do you approach building and hiring your team?

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