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.
I went through two 60-minute in-person interviews for the Software Engineer Intern role at Amazon. Both interviews were conducted directly with the team I had applied to, which I found to be a great sign — it meant the people interviewing me were the ones I'd potentially be working with.
The first interview was with a Senior Manager and had a strong behavioral focus. I was asked several questions tied to Amazon's Leadership Principles (LPs), so it's important to go in with well-prepared STAR-format stories that demonstrate things like ownership, customer obsession, and bias for action. Be ready to go deep on your examples — they will follow up and probe for specifics.
The second interview was with a Software Developer and blended both behavioral and technical elements. It started with a couple of LP-style questions before transitioning into a technical coding problem. The technical portion felt like a standard algorithmic problem — think data structures and problem-solving under time pressure. Make sure you can clearly communicate your thought process as you work through the problem, not just arrive at the answer.
Overall, the process felt well-structured and the interviewers were professional. My advice for anyone preparing: brush up on Amazon's Leadership Principles thoroughly, practice coding problems on platforms like LeetCode, and be ready to tie your technical and behavioral answers back to real experiences. The combination of both in a single interview can feel intense, so mock interviews that simulate this format are really helpful.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
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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.