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Amazon interview notes, page 61 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·Onsite - Coding / Algorithms

May 2026
Amazon SWE coding round, one question the whole time: implement a path-based getter for nested Maps and Arrays. Felt like a parsing problem dressed up as a data structures question, which I wasn't quite expecting.
  • Implement a get(object, path) function that retrieves a value from a nested structure of objects and arrays using a dot-and-bracket path string (e.g. ab[1].c.d[2][13]). Return the value if the path is valid, otherwise return null.

“My first instinct was to split on dots and then handle brackets separately, which got messy fast.”

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Amazon·Data Scientist·Technical Phone Screen

May 2026
Amazon Data Scientist technical screen covering a pretty wide range of ML fundamentals and computer vision stuff. Nothing felt like a trick but there was a lot of ground to cover in one sitting, which made it feel more like a quiz than a conversation.
  • What is the difference between overfitting and underfitting, and how do you detect and address each?
  • What is data augmentation and can you give some image-specific examples?
  • Walk me through the main components of a Convolutional Neural Network and explain what each part does.
  • How does a Recurrent Neural Network handle sequential data?
  • Explain the roles of positional embeddings, self-attention, residual connections, and feed-forward layers inside a transformer encoder.
  • What is dropout and why does it improve model generalization?
  • Compare bagging and boosting. How do they differ in terms of bias, variance, and how each algorithm behaves?

“Went okay.”

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Amazon·Software Engineer·Onsite - Behavioral / Leadership

May 2026
Amazon software engineer interview with a question focused on how you handle AI-generated output in a real production context. Pretty niche angle compared to the usual behavioral stuff, and it caught me a bit flat-footed.
  • Describe a time you had to verify AI-generated output before using or shipping it. How did you decide what to check, what methods did you use, and how did you handle any errors you found?

“This one tripped me up a little because I kept wanting to make it a story about the AI being wrong, but they seemed more interested in the process of deciding what even needed checking.”

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Amazon·Data Scientist·Technical Phone Screen

May 2026
Amazon Data Scientist technical screen, one meaty pandas question that looked straightforward on the surface but had enough moving parts to trip you up if you weren't careful about the groupby-rolling interaction.
  • Given a daily sales table with product IDs, units sold, and revenue, write pandas code to compute a 3-day rolling average of revenue per product, pivot the result so dates are rows and product IDs are columns, and merge in the total units sold per product.

“The rolling part is where I fumbled.”

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Amazon·Product Manager·Hiring Manager Screen

May 2026
First-round hiring manager screen for an Amazon Logistics PM role, heavy on behavioral questions tied to customer obsession and ownership. Four main prompts, each with follow-up probes about tradeoffs and metrics. Felt more like a structured debrief than a casual conversation.
  • How does your previous experience connect to this logistics PM role?
  • Tell me about a time you thought bigger than what the customer explicitly asked for.
  • Describe a time when the customer didn't actually understand their own need, and how you figured out what they really wanted.
  • Walk me through a time you dealt with a genuinely complex problem, including the ambiguity and competing priorities.

“I went in thinking a broad background summary would land well.”

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Amazon·Software Engineer·Onsite - System Design / Architecture

May 2026
System design round at Amazon for a SWE role, centered almost entirely on ride-hailing infrastructure. The interviewer clearly had a specific agenda around the GPS write path and geospatial indexing trade-offs, so if you walk in without that prep you're going to struggle.
  • Design a ride-hailing service where riders can request rides and get matched to nearby available drivers.
  • How would you handle high-frequency GPS updates from drivers without overwhelming your location index?
  • Compare Quadtree and H3 hex-grid indexing for storing and querying driver locations. What are the trade-offs?
  • How would you use Redis GEO to serve low-latency driver location reads under a heavy write load?
  • How do you keep driver availability state consistent, especially when a driver transitions from available to on-trip?

“The scope feels manageable until they start pulling on specific threads.”

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Amazon·Product Manager·Onsite - Behavioral / Leadership

May 2026
Interviewed for a PM role at Amazon. Just one behavioral question shared here but it was the kind that makes you pause and think about how you actually handle pushback from your own team.
  • How would you handle a situation where your team disagreed with your ideas?

“I went with a story about a roadmap call where engineering pushed back hard on a feature I was championing.”

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Amazon·Machine Learning Engineer·Technical Phone Screen

May 2026
Amazon ML engineer screen, just one question about a past project. Pretty standard but I felt like I rambled more than I should have.
  • Walk me through a machine learning project you've worked on and what came out of it.

“I picked a project I knew well but ended up spending too long on the problem setup and barely had time to talk about results.”

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Amazon·Data Scientist·Technical Phone Screen

May 2026
Amazon data scientist interview focused almost entirely on ML fundamentals through the lens of a churn prediction scenario. Four questions, all technical, and they kept the scenario consistent throughout which I wasn't expecting.
  • In a churn prediction problem with messy real-world data, how would you handle missing values in your training set and why?
  • For this churn prediction scenario, which ML algorithm would you pick and what's your reasoning?
  • Walk me through how Random Forest works, specifically the voting mechanism, how features are sampled at each split, and how tree depth is controlled.
  • How do you define overfitting and underfitting, and what methods do you use to detect and fix each?

“I went with imputation pretty quickly, mean for numerical stuff, mode for categoricals, but they pushed back and asked when that might actually hurt you.”

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Amazon·Software Engineer·Onsite - Behavioral / Leadership

May 2026
Two behavioral questions for a Software Engineer role at Amazon, both classic leadership principle territory. Nothing technically tricky but the follow-up probing on measurable outcomes caught me a bit flat-footed.
  • Tell me about a time you received negative feedback. What was the feedback, how did you respond, and what measurable change came out of it?
  • Describe a time you went beyond what was asked of you. Why did you do it, how did you manage the scope creep risk, and what was the actual impact?

“The part that tripped me up was 'measurable change.' I had a decent story about code review feedback but I'd never thought to quantify the outcome.”

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