Background
- Company: Amazon
- Role: SDE2 (Software Development Engineer 2)
- Team: Amazon Prime Video
- Location: Bangalore, India
- Joined: April 2026
- Total Experience: 3–4 years
- Application Process: Online application via Amazon Jobs portal → HackerRank assessment → 3 Onsite + 1 Virtual rounds
- Result: Selected ✅
Hackerrank Online assesment
💻 Online Assessment (HackerRank)
The assessment had 3 parts:
• Coding Challenge (90 min) — 2 LeetCode-style problems with a real compiler. Languages supported: C, C++, Java, Python, JavaScript, Go, Kotlin, and more.
• Work Simulation (15 min) — Software development decisions faced by SDEs at Amazon.
• Work Style Surveys (10 min) — 2 surveys on your engineering approach and work style.
💡 Update: My assessment followed the older format above. I've recently learned that Amazon has introduced a new assessment format which involves building code/applications using AI agents — aligning with Amazon's push towards Gen AI adoption across engineering. So if you're applying now, expect a different experience!
After clearing the online assessments I was called for onsite interviews
Round 1 — DSA / Problem Solving (1 hour)
Technical (30 min) + Leadership principles & GenAI (30 min):
Q1: Longest Strictly Increasing Subsequence with Maximum Adjacent Difference Constraint
Given an array of integers and an integer k, find the length of the longest subsequence such that:
- The elements are strictly increasing
- The difference between any two consecutive elements in the subsequence is at most k
- The relative order of elements in the original array is maintained (it's a subsequence, not a subarray)
Example 1:
Input: arr = [7, 1, 4, 5, 8, 8, 10, 6, 7, 7, 7, 8], k = 4
Output: 6
Explanation: The longest valid subsequence is [1, 4, 5, 6, 7, 8]
- 4 - 1 = 3 ≤ 4 ✓
- 5 - 4 = 1 ≤ 4 ✓
- 6 - 5 = 1 ≤ 4 ✓
- 7 - 6 = 1 ≤ 4 ✓
- 8 - 7 = 1 ≤ 4 ✓
All elements appear in the same relative order as in the original array.
Example 2:
Input: arr = [3, 1, 2, 6, 10, 11, 4, 5], k = 3
Output: 4
Explanation: One valid subsequence is [1, 2, 4, 5]
- 2 - 1 = 1 ≤ 3 ✓
- 4 - 2 = 2 ≤ 3 ✓
- 5 - 4 = 1 ≤ 3 ✓
Example 3:
Input: arr = [5, 4, 3, 2, 1], k = 2
Output: 1
Explanation: No two elements form a strictly increasing pair in subsequence order, so the longest valid subsequence has length 1.
Approach: This is a variation of the Longest Increasing Subsequence (LIS) problem with an additional constraint on the maximum allowed difference between adjacent elements in the subsequence.
Q2: Largest Subset of Binary Strings with Bounded Ones and Zeroes
Given an array of binary strings and two integers m and n, find the size of the largest subset such that:
- The total number of 1s across all strings in the subset is at most m
- The total number of 0s across all strings in the subset is at most n
Example 1:
Input: strs = ["100", "10", "1", "11", "111"], m = 3, n = 0
Output: 2
Explanation: The largest valid subset is ["1", "11"]
- Total 1s = 1 + 2 = 3 ≤ 3 ✓
- Total 0s = 0 + 0 = 0 ≤ 0 ✓
Note: ["111"] also satisfies constraints (1s = 3, 0s = 0) but has only 1 element.
Example 2:
Input: strs = ["10", "0001", "111001", "1", "0"], m = 5, n = 3
Output: 4
Explanation: The largest valid subset is ["10", "0001", "1", "0"]
- Total 1s = 1 + 1 + 1 + 0 = 3 ≤ 5 ✓
- Total 0s = 1 + 3 + 0 + 1 = 5... (other valid subsets exist)
Example 3:
Input: strs = ["10", "1", "0"], m = 1, n = 1
Output: 2
Explanation: The largest valid subset is ["1", "0"]
- Total 1s = 1 + 0 = 1 ≤ 1 ✓
- Total 0s = 0 + 1 = 1 ≤ 1 ✓
Leadership Principles + GenAi (30 min):
LP Focus: Ownership and deep dive
The interviewer asked me to describe a project where I took ownership to solve a critical issue that had a meaningful impact on users. Discussions involved follow-up questions.
Gen AI Question:
Tell me about a time u used Gen AI to improve your personal or team productivity
Round 2 — System Design (1 hour) Technical (30 min) + LP (30 min):
Design a Facebook-like News Feed System at Scale
Problem Statement:
Design a social media feed system (similar to Facebook) that supports millions of users who can post, view, and like content.
Functional Requirements:
- Users should be able to post any type of media (text, images, videos)
- Users should be able to view posts in their feed
- Like counts and view counts should be visible in real-time
- Users should be able to like and view posts with minimal latency
Non-Functional Requirements / Key Focus Areas:
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Scale: Handle massive traffic — millions of concurrent users posting, liking, and viewing
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Feed Loading Speed: The feed should render almost instantly upon login, even if the user has cleared their browser/app cache
Discussion Points & Follow-ups:
- API design
- Push vs. Pull model for feed generation — trade-offs of each
- Choice of databases — SQL vs. NoSQL vs. a combination and trade-offs
- Caching strategy — what to cache, invalidation policies, CDN usage for media
- Handling Viral Content (Celebrity Problem):
- A celebrity's post goes viral with millions of likes and views in seconds — how do you prevent this from becoming a bottleneck?
- Rate limiting, sharding/parition strategies, async processing of likes/view counters
- Monolithic vs. Microservices — and why?
- Service boundaries — how would you split responsibilities?
Leadership Principles + GenAi (30 min):
LP Focus: Have Backbone; Disagree and Commit
The interviewer asked me to describe a scenario where I had a difference in opinion with my team or manager while solving a critical problem, and how my approach ultimately helped resolve the issue.
Gen AI Question:
Tell me about a time you used Generative AI to solve a business problem and the measurable results it delivered.
Round 3 — System Design (1 hour)
Technical (30 min)+ LP (30 mins):
Design a Music Streaming Application (like Spotify)
Problem Statement:
Design a music streaming platform that allows millions of users to discover, search, and stream music seamlessly.
Functional Requirements:
- Users should be able to search for songs, artists, and albums
- Users should be able to create and manage playlists
- Users should be able to like/save songs and see their library
Non-Functional Requirements:
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Low latency playback — music should start playing within milliseconds of pressing play
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High availability — the service should be up 99.99% of the time
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Scale — support millions of concurrent listeners streaming simultaneously
Discussion Points & Follow-ups:
- API design
- How do you serve audio files efficiently to millions of concurrent users?
- CDN strategy for audio content distribution across geographies
- How and where to store millions of audio files (object storage, metadata DB)
- Choice of database for song metadata, user data, playlists
- Storing listening history and user preferences for recommendations
- How to design a fast search system across millions of songs, artists, and albums
- Indexing strategies, full-text search (Elasticsearch/similar)
- Load balancing and horizontal scaling of streaming servers
- Monolithic vs. Microservices — service boundaries (streaming service, search service, recommendation service, user service, playlist service)
Leadership Principles + GenAi (30 min):
LP Focus: Learn and Be Curious
The interviewer asked me to describe a scenario where I had to go outside my area of expertise or do something uniquely different to solve a problem in one of my projects.
Gen AI Question:
"Tell me about a time you used Generative AI to automate or streamline a workflow."
Round 4 — Bar Raiser (Virtual | 1 hour)
The Bar Raiser is strictly a senior engineer (SDE3+) with 10+ years of experience and always from outside your hiring team — ensuring a completely unbiased evaluation. This round has no fixed structure — it's entirely up to the interviewer.
- Project Deep-Dive & System Design Discussion (~30 min):
The interviewer asked me to pick one of my most fascinating projects from my resume — something that solved a critical issue. After I explained the project at a high level, we went into a deep architectural discussion covering:
- The system design choices I made and why
- How the components interacted at scale
- Trade-offs disucssions
- individual contribution ( should be deisgn level)
- Impact of the contribution ( always quantify this )
Behavioral / Leadership Principles (~10min):
LP Focus: Customer Obsession / Earn Trust
"Tell me about a time you directly interacted with the stakeholder of your project/work."
Quick DSA Question (~5-10 min) ( as time permited):
Search an Element in a Sorted Rotated Array
Given a sorted array that has been rotated at some pivot point, search for a target element and return its index. Return -1 if not found.
Gen AI Question:
"How do you be a compitent software engineer in this era of Gen AI?"
IMPORTANT TIPS
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After the interviewer presents the problem, don't jump straight into coding or designing. Take a moment to think, ask questions, and clarify before you begin.
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For coding the interview wont provide details like constraints /duplicates / inputs-output format etc. Sollution should be most efficent by time and memory complexity which you should be able to quote yourself.
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For design rounds , again ask for all requirements yourself and discuss any assumptions made. Pay attention to the interviewer's hints disguised as follow-up questions.
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The interviewer will move to the Leadership Principles part regardless — usually after 30 to max 40 minutes. If you spend too long on the technical section, you'll have less time to demonstrate your LP answers, which carry equal weightage. So, pace yourself.
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For LP rounds — use the STAR method religiously, prepare 6-8 strong stories from your experience that can map to multiple Leadership Principles, be specific with numbers and outcomes — vague answers don't leave an impression.
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No compiler — just a plain editor or whiteboard.
All onsite and virtual rounds use a plain text editor or whiteboard — there is no compiler or IDE. For DSA rounds, this means you'll need to write clean code from scratch and dry run your solution through examples manually on the editor/whiteboard. Practice coding without auto-complete or syntax highlighting beforehand.
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