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Amazon interview notes, page 100 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 single coding question around transaction log analysis. Pretty focused session, nothing too wild.
  • Given a set of API transaction logs, how would you identify the top API users by usage volume?

“Spent the first couple minutes just making sure I understood what 'top users' meant since that could be by request count, data volume, or something else entirely.”

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Amazon·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026
Behavioral round at Amazon for a software engineering role. Pretty much one big question about cross-team collaboration, which sounds easy until you're actually sitting there trying to pick the right story.
  • How do you collaborate with teammates, partner teams, designers, and PMs? Walk through a specific project where you can describe your role versus others', how you communicated and stayed aligned, any conflicts or trade-offs, and what came out of it.

“I picked a story I thought was solid but halfway through I realized I was spending way too much time on the setup and barely got to the conflict part, which is probably what they actually care about.”

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Amazon·Data Scientist·Technical Phone Screen

Jun 2026
Amazon data scientist technical screen, pretty deep on ML fundamentals. The whole session was basically one long gradient descent question that kept branching into regularization and learning rate theory. Felt like a math exam more than a job interview.
  • Write vectorized pseudocode for batch gradient descent to fit a linear regression model with an intercept term using mean squared error. Walk through each variable and step clearly, including inputs, outputs, and the update rule derived from the cost function.
  • Describe at least two stopping criteria for gradient descent and explain when you would prefer one over the other.
  • How do you choose a learning rate for gradient descent, and what are the tradeoffs between a constant rate versus a time-decayed schedule? How does feature scaling affect convergence?
  • Modify the mean squared error cost function to include L2 regularization, excluding the intercept term. Write the new gradient and explain how you would choose the regularization strength.

“This felt manageable at first but the 'clearly explain each step and variable' part is where I started rambling.”

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Amazon·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Amazon system design round, object-oriented modeling focus. The whole session was basically one big design question about a restaurant system and they kept drilling into the relationships between entities and how operations like billing and reservations actually flow through the model.
  • Design an object-oriented model for a restaurant system. Include entities like Restaurant, Table, Reservation, Menu, MenuItem, Order, OrderItem, Bill, Payment, Customer, and Staff roles such as Waiter, Chef, and Manager. Define the class relationships, key methods, and explain how the design supports reservation booking, order placement, kitchen ticketing, and billing workflows.

“This one ran the whole interview.”

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Amazon·Machine Learning Engineer·Onsite - System Design / Architecture

Jun 2026
Amazon ML Engineer loop, system design round focused entirely on GenAI. One question, but it was basically a full dissertation prompt crammed into a single ask. Walked out not totally sure if I nailed it or just survived it.
  • Walk us through a GenAI system you built from scratch. Cover the problem definition, data sourcing and governance, model selection, training or fine-tuning setup, evaluation, safety and guardrails, latency and cost constraints, and major failure modes. What trade-offs did you make, and how would you scale it to 10x traffic while hitting a 200ms p95 latency target and cutting costs by 20%?

“This question is basically five questions duct-taped together.”

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Amazon·Software Engineer·Onsite - Coding / Algorithms

Jun 2026
Amazon SWE interview with two coding problems, one algorithmic and one more design-adjacent. Nothing too outrageous but the second question had some annoying edge cases around graph traversal that I didn't fully think through in time.
  • You're given two arrays, one representing product sizes and one representing the cost to increment each product's size by one unit. Find the minimum total cost to make all sizes distinct.
  • Implement a movie recommendation function that takes a username and returns movie titles ranked by how often they were watched by that user's friends and friends-of-friends, using provided helper methods for fetching friends and watched movies.

“I knew this was a greedy or sorting problem pretty quickly but fumbled the tie-breaking logic.”

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Amazon·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026
Amazon behavioral round for a software engineer role, basically one long question about crunch time and how you handled it. Pretty standard LP stuff but the depth they want is real.
  • Tell me about a project where you had to deliver under a tight deadline. Walk through what you owned, how you dealt with limited resources, what you cut or deferred, how you kept stakeholders informed, and what came out of it.

“This is deceptively wide.”

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Amazon·Technical Program Manager·Onsite - System Design / Architecture

Jun 2026
Did a system design round for a TPM role at Amazon. Two questions, both tied together around a pretty specific product constraint. Not the hardest session I've had but the follow-up on traffic origin caught me more off guard than I expected.
  • How would you design and scale a messaging system that only supports emojis, targeting users in Europe and the US?
  • How would you determine where your traffic is originating from?

“The emoji-only constraint threw me for a second because it sounds trivial but it actually changes a lot about storage and encoding assumptions.”

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Amazon·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Amazon ML Engineer technical screen, pretty much a deep dive into reinforcement learning fundamentals. One meaty question that took up most of the time.
  • Walk me through the core components of a reinforcement learning setup: what makes up the underlying Markov decision process, what additional pieces does the agent itself define, and how does everything connect in the actual learning loop?

“This felt broader than I expected for a phone screen.”

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Amazon·Software Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at Amazon for a software engineer role. The problem felt completely made up, possibly AI-generated, and I had zero prior exposure to anything like it so I just kind of winged it.
  • Design a system that handles slow or long-running computations efficiently.

“Never seen a problem framed quite like this before.”

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