I recently completed the Virtual Onsite for the Amazon 2026 SDE Intern role. The process consisted of two technical rounds, and each round followed a similar format: Behavioral questions followed by a coding problem. Both interviewers were Chinese engineers, which made communication quite smooth and natural throughout the conversations.
Amazon interviews usually place significant emphasis on Leadership Principles, so the behavioral portion took a considerable amount of time in both rounds. The interviewers also asked many follow-up questions to explore the details behind each story. Below is a full recap of the interview process for reference if you are preparing for an Amazon SDE Intern VO.
Round 1
Behavioral Interview
The first round started with a behavioral question related to working under a tight deadline. The interviewer asked me to describe a situation where I had to complete a project within a limited timeframe.
After I introduced the background and explained the situation briefly, the interviewer began digging into details with several follow-up questions, such as:
- Who set the deadline for the project?
- How much time was actually available?
- Were you working on other tasks at the same time?
- How did you prioritize your responsibilities?
- If time was insufficient, did you consider alternative solutions?
The focus seemed to be on understanding the reasoning behind my decisions rather than just the story itself. Amazon interviewers often try to verify whether the situation is realistic and whether your actions demonstrate strong ownership and judgment.
The interviewer then asked about a time when I had a disagreement with a teammate. Similar to the previous question, this topic involved detailed follow-up discussions:
- What caused the disagreement
- What were the different perspectives
- How the communication happened
- How the conflict was eventually resolved
Finally, there was a more open-ended question about technology thinking:
“What kind of problems do you think AI could help solve in your work or projects?”
This question did not have a standard answer. The goal seemed to be evaluating whether candidates actively think about applying new technologies to improve productivity or solve real-world problems.
Overall, the behavioral portion of this round lasted around 30 minutes.
Coding
The coding problem in the first round was essentially a variation of the classic problem of finding the K-th largest element in an array. It was framed within a business scenario, but the algorithmic core remained the same.
There are several common approaches to solve this problem, including QuickSelect and Heap-based methods. I chose to implement the solution using a min-heap.
The idea is to maintain a heap with size k. As we iterate through the array, elements are pushed into the heap. If the heap size exceeds k, the smallest element is removed. After processing all elements, the top of the heap represents the k-th largest value.
The complexity of this approach is:
- Time complexity: O(n log k)
- Space complexity: O(k)
After finishing the implementation, the interviewer asked me to explain the complexity and then walk through a small example to verify the algorithm using a dry run.
The coding portion went smoothly and there were no major follow-up questions afterward.
Round 2
The second round took place on the same day and followed a very similar structure: behavioral discussion followed by a coding task.
Behavioral Interview
The main focus of this round was data-driven decision making. The interviewer asked me to describe a project where data analysis played an important role in guiding a decision.
I started by explaining the background, the data sources involved, and the objective of the analysis. After that, the interviewer continued asking detailed questions, including:
- How the data was collected
- What metrics were used to evaluate outcomes
- Whether any unexpected patterns appeared in the data
- How anomalies or inconsistent results were handled
Another important question was about mistakes made during the project. The interviewer wanted to know:
- What went wrong
- How the issue was discovered
- What steps were taken to fix it
- What improvements would be made if the project were repeated
This behavioral section also lasted close to half an hour and involved detailed discussion around decision-making processes.
Coding
The coding question in the second round was not a direct LeetCode problem. It required solving a state search problem using a breadth-first search approach.
The task involved starting from an initial state and exploring possible transitions step by step until reaching a valid target condition.
The typical approach is to use a queue to perform BFS traversal. For each state removed from the queue, new states are generated and added back into the queue for further exploration. A visited set is necessary to avoid repeated processing of the same state.
The overall implementation was straightforward. The main challenges were correctly managing state transitions and handling edge cases.
After completing the code, the interviewer briefly checked the logic but did not add additional follow-up questions.
Overall Thoughts
The overall difficulty of this Amazon SDE Intern VO felt reasonable. The behavioral portion was heavily emphasized, while the coding problems focused on classic algorithm patterns rather than extremely tricky questions.
If you are preparing for Amazon interviews, it is helpful to review common topics such as heaps, graph traversal, and breadth-first search. Equally important is preparing several well-structured behavioral stories that can withstand detailed follow-up questions.
If you feel uncertain about how to prepare or worry about getting stuck during an interview, you can also explore external guidance and practice resources. Our team continuously organizes interview questions from companies such as Amazon, Google, Citadel, and Uber. We also provide real-time interview guidance and idea support to help candidates stay on track and navigate difficult moments during OA or virtual interviews.
Top comments (0)