Amazon Software Engineer interviews typically run 4–6 rounds: online assessment, coding, system design, behavioral, and a bar raiser. The process spans several weeks and distinctively embeds Leadership Principles questions into nearly every round.
$135K
Avg. Base Comp
$274K
Avg. Total Comp
4-6
Typical Rounds
4-12 weeks
Process Length
Amazon's interview process has one defining characteristic that catches many candidates off guard: Leadership Principles aren't confined to a single behavioral round. They're woven into nearly every interview. Multiple candidates reported LP questions appearing in technical rounds, design discussions, and bar raiser sessions that also included coding. One candidate counted 13 to 15 LP questions across a single loop. This isn't incidental — it reflects how Amazon actually evaluates fit. We've seen candidates who solved every coding problem correctly still not receive offers because their LP stories lacked specificity or didn't map cleanly to Amazon's values.
On the technical side, the coding questions tend to land at medium difficulty, but the breadth of what's tested is what makes the loop demanding. Candidates encountered classic DSA problems like graph traversal, dynamic programming, and sliding window, alongside less conventional prompts — including partition DP problems, AI-assisted coding questions, and a full-stack troubleshooting task with a restricted AI helper. A recurring theme is that Amazon wraps problems in long narrative scenarios, which adds a reading-comprehension layer on top of the actual algorithm. Several candidates independently flagged this as disorienting, particularly in the online assessment. The design rounds also surprised people: multiple candidates expected infrastructure-heavy system design and instead got class design, API design, and practical object modeling — designing a flight seat selector or an in-memory file system rather than a distributed cache.
Perhaps the most telling pattern: multiple candidates described nearly identical preparation yet had divergent outcomes across different attempts at the same role. One candidate explicitly received an offer in one process and a rejection in another. This points to how much consistency across the full loop matters — a single weak LP answer or a design discussion where tradeoffs weren't articulated clearly can tip the balance. The bar for structured, clear communication is genuinely high throughout every round, not just the behavioral ones.
Synthetized from 20 candidates reports by our editorial team.
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Real interview reports from people who went through the Amazon process.
My process with Amazon started with an online assessment from home, which took about 1 hour and 45 minutes. It had two medium LeetCode-style coding questions, but both came with a long background story that made the setup a little confusing at first. After the coding portion, there was also a behavioral segment that lasted around 20 minutes and focused on Leadership Principles, including questions that felt tied to email interaction and how I handled communication in work situations. After passing the OA, I was invited to the interview loop about a month later. In my case, there were two interview rounds, and one of the experiences I had was a 60-minute back-to-back session with two engineers. That round mixed behavioral questions with technical ones, and each interviewer asked a technical problem similar to LeetCode or class design. The behavioral part was pretty standard Amazon-style LP questioning, so I spent a lot of time explaining past decisions, collaboration, and ownership. Another technical round was less conventional and included an AI-assisted coding question, which stood out because it wasn’t just a straightforward algorithm problem. The coding questions overall were not the usual pattern-matching interview problems. One of them was described as unconventional and required more creative thinking and problem-solving than relying on a memorized data structure template. In another technical discussion, I was asked a partition dynamic programming problem, where I had to think through how to divide an array or string into segments and define the right state and recurrence. That round also included several Generative AI theory questions, like LLMs, the difference between traditional machine learning and generative AI, RAG, and hallucinations. The interviewer seemed more interested in clear reasoning and practical understanding than in deep theory. Overall, I’d say the process was moderate to hard, mostly because the technical questions were a bit unusual and the behavioral portion was very Amazon-specific. I didn’t get an offer in my case, but the offer outcome seemed to depend heavily on both technical clarity and Leadership Principles. My advice would be to prepare for medium LeetCode problems, practice explaining your thought process clearly, review Amazon LPs carefully, and be ready for at least one question that feels more open-ended or unconventional than standard interview prep.
Prep tip from this candidate
Practice explaining your reasoning process clearly on medium LeetCode problems and unconventional coding questions. Study Amazon's Leadership Principles deeply, especially around communication and ownership, as they heavily influence hiring decisions alongside technical performance.
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Topics based on recent interview experiences.
Featured question at Amazon
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Synthesized from candidate reports. Individual experiences may vary.
Initial call with a recruiter covering your background, the role, and basic fit. Recruiters are generally helpful and may share guidance on what to expect in later stages. Some candidates also receive a personality or work-style alignment survey at this stage.
A take-home coding assessment with two LeetCode-style algorithmic problems (typically easy to medium difficulty, sometimes wrapped in long background scenarios), followed by a section where you explain your approach and time/space complexity. Most versions also include a work style survey and behavioral/Leadership Principles questions; some include a full-stack troubleshooting task with a restricted AI helper.
A conversation with the hiring manager covering your background, past experience, and team fit. This stage appears for some candidates after the OA before advancing to the full loop, though it is not universal across all teams and may be folded into the onsite loop.
Typically 3-4 back-to-back video rounds covering DSA coding (medium to hard LeetCode-style problems including graphs, DFS, dynamic programming, sliding window, and linked lists), practical API and class or low-level design, and system design discussions often tied to real project experience. Nearly every round includes 2-3 Amazon Leadership Principles behavioral questions alongside the technical content, and some loops include questions on Generative AI concepts such as LLMs and RAG.
A dedicated round conducted by a trained Bar Raiser that focuses heavily on Amazon Leadership Principles, with questions around ownership, failure, conflict, and prioritization, while still including at least one coding problem. This round carries significant weight in the final hiring decision.