I finally wrapped up all four rounds of Amazon's 2026 New Grad Virtual Onsite interview last month. It took me a while to recover before writing this recap. Instead of another round-by-round interview diary—which there are already plenty of online—I wanted to focus on the three moments that stood out the most. Each of them reflects a real change in how Amazon interviews candidates this year. I'll still mention the coding questions, but the emphasis is on the parts that most interview reports rarely talk about.
Moment 1: "What if AI-generated code ships with a bug?"
The third interview was with a senior engineer who appeared very approachable. The conversation started with standard Leadership Principle questions before he casually asked:
"Do you use GenAI tools?"
I assumed it was simply small talk and mentioned that I regularly use GitHub Copilot. That turned out to be only the beginning.
The follow-up questions became increasingly specific:
- What are the limitations of large language models?
- How do you deal with hallucinations?
- What do you focus on when reviewing AI-generated code?
- If AI-generated code passes review but later causes production issues, how would your debugging process differ from debugging human-written code?
- If you had to design a metric to evaluate AI-assisted software development, what would you measure?
I can confidently say these are not questions you can memorize from interview guides. My answers came entirely from personal experience. When reviewing AI-generated code, I explained that I pay extra attention to edge cases and hidden assumptions because AI tends to make mistakes differently than human engineers.
For the productivity metric, I suggested measuring the reduction in repetitive boilerplate code, since that is one of the first areas where AI changes engineering workflows. The interviewer became genuinely interested, and we spent nearly fifteen minutes discussing the topic.
Looking back, the message was clear: Amazon is beginning to treat AI-assisted software development as an interview topic rather than a casual conversation.
My advice is simple: Don't just read about AI tools—actually use them for several weeks before interviewing. Real experience is very difficult to fake once interviewers begin asking follow-up questions.
The coding question in this round was LeetCode 31 — Next Permutation. The standard three-step solution (find the pivot, swap, reverse the suffix) was sufficient.
Moment 2: I Got Stuck on an Elevator Scheduling Problem
The final Bar Raiser interview included an elevator scheduling problem.
This was not an object-oriented design interview. There were no UML diagrams or class hierarchies. Instead, it was a pure implementation problem centered around scheduling logic.
The interviewer defined several core functions with fairly realistic constraints:
- Passengers inside and outside the elevator can request floors simultaneously.
- Arriving at a floor should trigger arrival events.
- The scheduler must determine travel direction dynamically.
- Multiple passengers may be waiting on the same floor.
I implemented a scanning algorithm using separate priority queues for upward and downward requests. Everything went smoothly until I reached the transition logic: What should the elevator do when it is moving upward but no requests remain above its current position?
I froze for over a minute because I had two competing approaches in mind.
What ultimately saved me was the interviewer's hint. After following his suggestion, I adjusted the scheduling logic and finished the implementation successfully. His final feedback was that the overall structure was clean and that I handled edge cases well.
This experience taught me something important: Getting stuck isn't what hurts your interview. What matters is what you do while you're stuck.
Instead of remaining silent, I openly explained both candidate solutions and discussed the trade-offs between them. That transparency encouraged the interviewer to guide me in the right direction.
If you freeze during an interview, verbalizing your thought process is far better than silently trying to appear confident.
Interestingly, I had encountered a similar scheduling problem during a mock interview on InterviewShow. That previous experience didn't help because it predicted the exact question—it helped because I'd already experienced the feeling of getting stuck and recovering under pressure. That's the real value of mock interviews.
This round also included a variation of the Celebrity Problem, solved using the standard elimination approach.
Moment 3: One Leadership Principle Story Was Questioned Five Levels Deep
The behavioral section of the Bar Raiser interview focused on one of my strongest Ownership stories. I thought I had prepared it extremely well.
Then the follow-up questions started.
- "What happened next?"
- "Was that decision made by you or by the team?"
- "You said the result was successful. Do you have numbers?"
- "If you could do it again, what would you change?"
- "What permanent process improvements came out of this project?"
By the third follow-up, I had exhausted my rehearsed version of the story. Everything afterward relied entirely on my genuine memory of the project.
That, in my opinion, is exactly how Bar Raisers evaluate candidates. They aren't listening for polished storytelling. They're stress-testing authenticity. Fabricated experiences rarely survive five layers of detailed questioning because people simply don't invent operational details like long-term process improvements.
Because of that, my preparation advice differs from most interview guides. Instead of preparing ten perfectly polished stories, prepare four or five projects that you genuinely understand. Review every decision, every metric, every trade-off, and the complete timeline.
I also strongly recommend reading Amazon's official Leadership Principles in English. The behavioral descriptions beneath each principle are effectively the grading rubric. For example, Ownership includes the mindset of never saying "that's not my job," and many follow-up questions naturally evolve from that expectation.
The Other Interview Rounds
The first two rounds were relatively standard.
- Round 1: LeetCode 994 — Rotting Oranges (multi-source BFS).
- Round 2: Course Schedule (topological sorting) followed by a harder Trie-based Search Suggestions System variant that required returning the lexicographically smallest three suggestions for every prefix.
Time was tight during the Trie implementation, so I continuously explained my data structure choices while coding. All test cases passed in the end.
Final Thoughts
If I had to summarize Amazon's 2026 interview process in one sentence, it would be this: Grinding LeetCode only prepares you for half of the interview.
Coding skills are still essential, but interviews increasingly evaluate three additional abilities:
- Real experience working with AI development tools.
- The ability to withstand deep Leadership Principle questioning.
- Your recovery process when you become stuck during a live interview.
The two most difficult moments in my interview—the elevator scheduling problem and the five-level LP interrogation—were manageable only because I had already experienced similar situations during mock interviews on InterviewShow. Their Amazon-focused mock interviews, especially the Leadership Principle coaching and realistic Bar Raiser follow-up sessions, were the parts I found most valuable.
Everyone prepares differently, but I strongly believe in one principle: Get challenged in a realistic mock interview before facing the real one.
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