Amazon·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Amazon SWE interview with a mix of classic hard leetcode problems and a practical system design question about their locker product. Nothing too surprising but the locker design was a nice change of pace from pure algo grind.

Questions Asked (3)

Given an array of integers, for each element count how many numbers to its right are smaller than it. Return the result as an array.

Algorithms & Data Structures

Author's notes

This is a brutal one if you haven't seen it before.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and constraints, then propose an efficient solution using a modified merge sort or a Fenwick tree (Binary Indexed Tree) to count smaller elements to the right. Discuss the time and space complexity, and be prepared to code the solution.

Pro tip: Mention that this is a classic problem that can be solved with a Fenwick tree or merge sort, and that Amazon often values optimization and clean code. Also, consider edge cases like duplicate elements and large input sizes.

1. Understand the problem

Restate the problem in your own words and ask clarifying questions about input size, range of integers, and whether duplicates are allowed.

2. Discuss brute force approach

Acknowledge that a naive O(n^2) solution exists by checking each element against all elements to its right, but note it's inefficient for large inputs.

3. Propose optimized solution

Explain that you can use a Fenwick tree (BIT) or modified merge sort to achieve O(n log n) time complexity. Describe the algorithm briefly.

4. Analyze complexity and edge cases

State the time and space complexity of your solution, and discuss how to handle duplicates and negative numbers.

5. Code and test

Write clean code for the chosen approach, and walk through a small example to verify correctness.

Key Points to Mention

Time complexity: O(n log n) for optimized solution vs O(n^2) for brute force
Space complexity: O(n) for auxiliary data structures
Handling duplicates: ensure counts are correct when equal elements exist
Choice of data structure: Fenwick tree (BIT) or merge sort with index tracking
Edge cases: empty array, single element, all elements same, large input
Amazon leadership principles: customer obsession (optimize for performance), dive deep (explain trade-offs)

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

Merge k sorted linked lists into a single sorted linked list.

Algorithms & Data Structures

Author's notes

Pretty standard at this point.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (e.g., k and list sizes) and discussing naive approaches like merging one by one. Then present the optimal heap-based solution, explaining its time and space complexity, and walk through a concrete example. Finally, mention edge cases and potential optimizations.

Pro tip: Amazon values customer obsession and ownership; emphasize how your solution scales efficiently for large inputs, showing you consider real-world impact. Also, proactively discuss trade-offs between different approaches (e.g., heap vs. divide-and-conquer) to demonstrate depth.

1. Clarify requirements and constraints

Ask about the range of k, list lengths, memory limits, and whether the lists are sorted in ascending order. Confirm the expected output format.

2. Discuss naive approaches

Mention the simple approach of merging lists sequentially (O(N*k) time) and its inefficiency. This shows you consider multiple solutions before optimizing.

3. Present the optimal heap-based solution

Explain using a min-heap of size k to repeatedly extract the smallest node and append to the result. Detail the algorithm steps and why it achieves O(N log k) time.

4. Analyze complexity and edge cases

State time and space complexity. Discuss edge cases: empty lists, k=0, k=1, duplicate values, and very large k.

5. Mention alternative approaches and trade-offs

Briefly describe divide-and-conquer (pairwise merging) with O(N log k) time and O(1) extra space, and compare with heap approach.

Key Points to Mention

Min-heap (priority queue) to efficiently track the smallest current node among k lists.
Time complexity: O(N log k) where N is total number of nodes, and space complexity: O(k) for the heap.
Handling edge cases: empty input, k=0, k=1, and lists of varying lengths.
Divide-and-conquer approach: merge lists in pairs iteratively, achieving O(N log k) time and O(1) extra space.
Stability and handling duplicate values: the algorithm preserves relative order if using stable merging.
Real-world scalability: why O(N log k) is efficient for large k and N, and how it applies to Amazon's large-scale systems.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

Design the getPackage operation for Amazon's locker system, where a customer retrieves their package from a locker.

System DesignTechnical Trade-offsData Modeling

Author's notes

This was the most interesting part.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then design the core flow for package retrieval, including authentication, locker interaction, and failure handling. Focus on scalability, reliability, and security while discussing trade-offs in data modeling and system architecture.

Pro tip: Emphasize idempotency and graceful degradation—customers may retry or encounter network issues, so the system must handle duplicate requests and partial failures without compromising security or user experience.

1. Clarify Requirements

Ask about scale, latency, security, and edge cases (e.g., expired codes, locker malfunctions) to scope the problem and align with interviewer expectations.

2. High-Level Design

Outline the main components: customer app, backend service, locker hardware interface, and database. Describe the end-to-end flow from code entry to package retrieval.

3. Data Modeling

Define key entities (Package, Locker, Customer, AccessCode) and their relationships, including statuses and TTL for codes. Discuss storage choices (SQL vs NoSQL) and indexing.

4. API and Interaction Design

Specify the getPackage API endpoint, request/response format, authentication, and idempotency. Detail how the backend validates the code and triggers locker opening.

5. Scalability and Reliability

Address partitioning, caching, retries, monitoring, and failure recovery. Discuss trade-offs like consistency vs availability and how to handle concurrent access.

Key Points to Mention

Authentication and authorization: validate customer identity and access code, prevent unauthorized access.
Idempotency: ensure repeated getPackage requests with the same code don't cause issues (e.g., open locker multiple times).
Locker state management: track locker occupancy, door status, and sensor feedback to confirm package retrieval.
Failure handling: network timeouts, locker offline, code expired, package already retrieved—define fallback and retry strategies.
Data consistency: use transactions or distributed locks to avoid race conditions when multiple requests target the same locker.
Scalability: design for high throughput (e.g., peak seasons) with sharding, caching, and asynchronous processing.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.