HackTheRounds Interview Experiences

Amazon SDE-1 Interview Experience (2026) - Coding, GenAI & Bar Raiser, Rejected

A four round Amazon SDE 1 loop covering multi source BFS, graph and DP problems, practical GenAI judgment, Leadership Principles, and a difficult Bar Raiser.

By HackTheRounds Team · 2026-05-05

Background

I entered Amazon's University Talent Acquisition process with about a year and a half of frontend engineering experience. My preparation was split between common coding patterns, Amazon-tagged practice questions, basic design, and a bank of Leadership Principles stories. That balance mattered: this was not four hours of algorithms. Behavioral follow-ups appeared throughout the loop, and one round explicitly tested how I use generative AI in engineering work.

The process ended with a rejection, but only after I cleared three rounds and reached the Bar Raiser. My final interview was May 5, followed by almost a month without a decision. This is the round-by-round account I wish I had before starting.

Round 1: Coding - Multi-Source BFS

Problem: Given a grid containing empty cells, fresh oranges, and rotten oranges, return the minimum number of minutes needed for the rot to reach every fresh orange. Return -1 if some fresh orange can never be reached.

Practice it: [[problem/131?company=3|Rotting Oranges]]

I recognized that this was not a separate search from every rotten cell. All initially rotten oranges should enter the queue together, and each breadth-first layer represents one minute. I counted fresh cells up front, marked them as they entered the queue, and used the remaining count to decide whether the final answer was possible.

The interviewer cared about the explanation before the code: why this is multi-source BFS, why a cell should be enqueued only once, and how the all-empty and already-complete cases behave. The implementation was linear in the number of cells and the round ended cleanly.

Round 2: Graphs and Dynamic Programming

The second round combined two substantial problems. The graph task was a course-ordering problem: return a valid ordering when prerequisites form a directed graph, or report that no ordering exists if there is a cycle.

Practice it: [[problem/84?company=3|Course Schedule II]]

I discussed both DFS coloring and Kahn's algorithm. I chose the indegree-based approach because the returned topological ordering falls out naturally as nodes reach indegree zero. The essential check is that the output contains every course; a shorter output means a cycle prevented the remaining nodes from being scheduled.

The second task concerned tiling a board with dominoes and trominoes. Here, stating the dynamic-programming state was more important than rushing into code. I explained what each state represented, built the recurrence from smaller widths, and called out the need for a modulo operation and careful base cases. The interviewer repeatedly asked for the reasoning behind the recurrence rather than accepting a memorized formula.

Round 3: GenAI, Leadership Principles, and Coding

This hybrid round was the least documented part of the process. It opened with Ownership and Dive Deep stories, then moved into practical AI questions:

  • Where do I use an LLM in a normal development workflow?
  • What causes hallucinated output?
  • How do I validate generated code before trusting it?
  • What are the limitations of prompt engineering?

I framed AI as an accelerator for exploration, test generation, and unfamiliar-code explanation—not as an authority. My validation loop included reading the diff, checking assumptions against documentation, adding tests for edge cases, and keeping sensitive data out of prompts. The questions were conceptual rather than mathematical; the signal was whether I could use the tool with engineering judgment.

The coding portion was Next Permutation. Because the interviewer arrived late, I was offered a choice between completing every line or explaining the algorithm precisely. I walked through finding the rightmost pivot, swapping it with the smallest larger value to its right, and reversing the suffix. I also covered the descending-array case, where the next ordering wraps to the smallest permutation.

Round 4: Bar Raiser

The Bar Raiser began with a deep behavioral interview. Each answer triggered several follow-ups about my individual contribution, evidence, tradeoffs, and what I would change. A polished STAR outline was only the starting point; vague transitions or team-level claims were quickly examined.

The technical problem was a variation of the Celebrity Problem. I needed hints to establish the elimination step and then verify the remaining candidate. That made this my weakest round. The difficulty was not just arriving at a solution, but staying structured after my first idea failed and incorporating feedback without losing the thread.

Result and Timeline

The last round ended on May 5. My application remained under consideration while the recruiter said the interviews were still being evaluated. A promised update date passed, and the rejection arrived nearly a month after the loop.

The wait did not contain a reliable signal. A delayed answer can mean scheduling, calibration, headcount, or a close decision; it is not evidence of either an offer or a rejection.

Tips

  1. Make BFS, topological sorting, and core DP states automatic enough that you can explain them while coding.
  2. Prepare Leadership Principles stories with numbers, alternatives, and a clear description of what *you* did.
  3. Treat a GenAI interview as an engineering-responsibility discussion. Be ready to explain verification, privacy, and failure modes.
  4. In the Bar Raiser, say when an approach is not working, reset explicitly, and use the interviewer's question as new information.
  5. Do not read a verdict into recruiter silence. Keep other processes moving until there is a written decision.