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Amazon interview notes, page 110 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 SWE interview with a graph theory question that seems deceptively simple but has a few more angles than you'd expect.
  • How can you determine that a given graph is not a tree?

“I started rattling off the obvious stuff about cycles and then kind of stumbled when I realized there are multiple ways a graph fails to be a tree.”

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Amazon·Software Engineer·Technical Phone Screen

Jun 2026
Amazon SWE interview with a classic OOP theory question plus a live coding component. Pretty standard technical phone screen stuff, though the combination of explaining concepts AND writing code in the same question caught me slightly off guard.
  • Explain the difference between inheritance and polymorphism in OOP, then write a code example where a child class overrides a parent method and that override gets called polymorphically.

“I knew the definitions fine but fumbled a bit when they asked me to show polymorphism in the code itself, not just write a subclass.”

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Amazon·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Amazon system design round focused on payment infrastructure for Kindle. Pretty open-ended prompt with a lot of directions you could take it, which was both freeing and a little paralyzing.
  • Design a payment system for Amazon's Kindle platform.

“I started with the user flow, which felt safe, but I think I spent too long on the checkout UX side and not enough on the backend reliability stuff like idempotency, retry logic, and failure handling.”

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Amazon·Software Engineer·Online Assessment (OA)

Jun 2026
Amazon SWE online assessment with a scheduling/parallelism problem. Pretty classic OA format, nothing too surprising, but the problem had some nuance that I didn't fully think through at first.
  • Given an array of server request processing times and a maximum parallelism limit, find the minimum total processing time when no more than maxRequests requests can run simultaneously.

“My first instinct was greedy, sort and batch.”

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Amazon·Product Manager·Onsite - Behavioral / Leadership

Jun 2026
Amazon PM interview with a behavioral question tied to their leadership principles. Pretty standard stuff but process-building questions always trip me up more than I expect.
  • Describe a time when you built a process from scratch.

“I had an answer prepared but it felt thin the moment I started talking.”

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Amazon·Product Manager·Technical Phone Screen

Jun 2026
Amazon PM interview, one question about the difference between APIs and SDKs. Pretty light on detail but it's the kind of technical concept question that trips up non-technical PMs more than it should.
  • What is the difference between an API and an SDK?

“Classic 'do you actually understand how products are built' check.”

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Amazon·Software Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at Amazon for a software engineering role. The prompt was a full pricing engine for a ride-hailing platform, which sounds scoped until you realize they want carpooling logic, fairness constraints, experimentation hooks, and real-time updates all in one go. Dense question, not a lot of hand-holding.
  • Design and implement a pricing engine for a ride-hailing platform. The system should return multiple candidate routes via an API, compute prices per route using factors like distance, duration, supply/demand, wait time, and driver incentives, support carpooling with shared-segment pricing, enforce fairness constraints like surge caps and neighborhood equity, allow pricing experimentation, and handle real-time demand and driver availability updates. Include a high-level architecture and core price calculation code.

“This one ate up the whole session.”

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Amazon·Software Engineer·Technical Phone Screen

Jun 2026
Amazon SWE coding round, one meaty dedup problem that kept growing the longer we talked. The core question wasn't too bad but the parallelism follow-up is where things got interesting.
  • Given a list of events where each event has an ID and a timestamp, deduplicate the list so that only the most recent occurrence of each event ID is kept. Walk through your approach, edge cases, and how you'd handle a parallelized version across multiple workers.

“Started with sorting by timestamp and building a map from event ID to the latest event seen.”

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Amazon·Data Scientist·Onsite - Behavioral / Leadership

Jun 2026
Behavioral round for a Data Scientist role at Amazon. The whole thing was basically one long leadership principles stress test, with a follow-up that turned the original scenario into a mini crisis simulation.
  • Tell me about a time you had to enforce a strict, non-negotiable policy that your team pushed back on, and you still delivered results. Walk through the situation with specifics: the timeline, why the rule existed, who you had to bring along, what you were measured on, any ethical or risk considerations, options you ruled out, and the final quantified impact. Then reflect on what you'd do differently.
  • Follow-up: if two senior stakeholders gave you conflicting directions mid-project and your key metrics had been declining for two straight weeks, how would you get everyone realigned and turn the numbers around without violating the original constraint?

“This one took me longer to set up than I expected.”

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Amazon·Data Scientist·Technical Phone Screen

Jun 2026
Amazon data scientist interview, all technical, no behavioral fluff. They ran through ML fundamentals pretty systematically and I left feeling like I'd done okay on some and fumbled others.
  • Can you explain the bias-variance tradeoff and how it connects to overfitting and underfitting?
  • What is regularization and what problem does it solve? Walk me through L1 vs L2 and other approaches like early stopping.
  • When dealing with imbalanced datasets, which evaluation metrics do you reach for and why? How do accuracy, precision, recall, F1, PR-AUC, and ROC-AUC compare?
  • Define precision and recall with their formulas. In what situations would you prioritize one over the other?
  • In logistic regression, what does the model output before you convert it to a probability, and how does that conversion happen?
  • What does a ROC-AUC of 0.8 actually mean intuitively, and what's a caveat you'd flag?
  • What are ensemble models and why do they tend to outperform individual models?
  • For tree-based models like decision trees, random forests, and gradient boosting, what are the key hyperparameters and how do they push the model toward higher bias or higher variance?
  • Compare sigmoid and softmax activations. When do you use each, and how do their outputs differ?

“This felt like a warmup but I still managed to ramble.”

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