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.
$160K
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.
Synthesized from 20 candidate reports by our editorial team.
Had an interview recently?
Share your experience. Unlock the full guide.
Real interview reports from people who went through the Amazon process.
The online assessment was the part I underestimated. After an initial recruiter conversation, I received an assessment with two sections: a 45-minute coding problem and a one-hour repository exercise where I had to find and fix bugs in an existing project. The coding prompt was written as a long, realistic Amazon scenario, so a lot of the difficulty came from processing the requirements quickly rather than from an especially obscure algorithm. The repository section felt closer to day-to-day engineering work because I had to understand unfamiliar code before deciding what to change.
After that, I moved into interviews with the hiring team covering coding, design, and behavioral topics. The live coding questions were generally variations on familiar patterns, but the interviewers expected more than a working first answer. One question involved finding the height of a tree, and I was asked to explain the different approaches rather than stopping after one solution. Other algorithmic material included a dynamic-programming problem combined with binary search and a Top K variation whose prompt did not provide a ready-made API. There was also a scenario about a restaurant manager handling groups as they arrived. The challenge was often translating an operational story into the right data structure and algorithm under time pressure.
The design portion included low-level and high-level design. I was asked how I would design a Dropbox-like service, and another scenario involved processing tasks arriving within a window while respecting a token limit. The behavioral discussion was woven into the technical rounds as well as covered directly. It focused on Amazon’s Leadership Principles, my past projects, how I approach problems, and situations involving communication or collaboration. I was specifically asked about a disagreement with a colleague and about difficulties I have encountered when using AI. The interviewers were professional and organized, and they were clear about the role and next steps, but I ultimately did not receive an offer. My main takeaway is that Amazon’s prompts can be deliberately wordy and practical, so it is important to clarify requirements quickly, explain alternative solutions, and prepare concrete Leadership Principles stories that can stand up to follow-up questions.
Prep tip from this candidate
Practice extracting requirements from long, real-world coding prompts within a 45-minute limit, and rehearse debugging an unfamiliar repository in about an hour. Be ready to compare multiple approaches to tree problems, work through Top K and DP-plus-binary-search variations, design a Dropbox-like service, and answer Leadership Principles follow-ups about workplace disagreements and AI usage.
Share your own interview experience to unlock all reports, or subscribe for full access.
Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Amazon
Select the 2nd highest salary in the engineering department
| Question | |
|---|---|
| Closest SAT Scores | |
| Empty Neighborhoods | |
| Subscription Overlap | |
| Top Three Salaries | |
| Monthly Customer Report | |
| Merge Sorted Lists | |
| Rolling Bank Transactions | |
| Customer Orders | |
| Comments Histogram | |
| Random SQL Sample | |
| Prime to N | |
| Upsell Transactions | |
| Top 3 Users | |
| Recurring Character | |
| Download Facts | |
| Address Schema | |
| Retailer Data Warehouse | |
| Permutation Palindrome | |
| Find the First Non-Repeating Character in a String | |
| Bagging vs Boosting | |
| Average Quantity | |
| Daily Retention Summary | |
| Manager Team Sizes | |
| Size of Joins | |
| Month Over Month | |
| P-value to a Layman | |
| Flight Records | |
| Get Top N Frequent Words | |
| Real-Time Transaction Streaming |
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.
