Amazon SDE-2 interview experience - 2026, Exp- 3 to 4 yrs

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 assessment

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.

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:

1. The elements are strictly increasing
2. The difference between any two consecutive elements in the subsequence is at most k
3. 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]

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]

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:

1. The total number of 1s across all strings in the subset is at most m
2. 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"]

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"]

Example 3: Input: strs = ["10", "1", "0"], m = 1, n = 1 Output: 2 Explanation: The largest valid subset is ["1", "0"]

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 you 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 can post any type of media (text, images, videos)
• Users can view posts in their feed
• Like counts and view counts should be visible in real‑time
• Users can like and view posts with minimal latency Non‑Functional Requirements / Key Focus Areas:
• Scale: Handle massive traffic — millions of concurrent users posting, liking, and viewing
• 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)
• Rate limiting, sharding/partition strategies, async processing of likes/view counters
• Monolithic vs. Microservices — why?
• Service boundaries — how to 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 can search for songs, artists, and albums
• Users can create and manage playlists
• Users can like/save songs and see their library Non‑Functional Requirements:
• Low latency playback — music should start playing within milliseconds of pressing play
• High availability — the service should be up 99.99% of the time
• Scale — support millions of concurrent listeners streaming simultaneously Discussion Points & Follow‑ups:
• API design
• How to serve audio files efficiently to millions of concurrent users?
• CDN strategy for audio content distribution across geographies
• Storage of 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
• Designing 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.

1. 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:

• System design choices I made and why
• How the components interacted at scale
• Trade‑offs discussions
• Individual contribution (should be design level)
• Impact of the contribution (always quantify this) Behavioral / Leadership Principles (~10 min): 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 permitted): 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 competent software engineer in this era of Gen AI?"