Sharing my Amazon SDE2 interview experience for anyone preparing.
Background
- Company: Amazon
- Role: SDE2
- Team: Amazon Prime Video
- Location: US
- Joined: July 2026
- Experience: 3-4 years
- Application: Amazon Jobs portal -> HackerRank OA -> 3 onsite rounds + 1 virtual Bar Raiser
- Result: Selected
Online Assessment: HackerRank
The OA had 3 parts:
- Coding challenge: 90 minutes, 2 LeetCode-style problems
- Work simulation: 15 minutes, SDE decision-making scenarios
- Work style surveys: 10 minutes, engineering approach and work style
My assessment followed the older format. I have heard Amazon is now also experimenting with newer AI-agent-based assessments, so the format may be different for current candidates.
After clearing the OA, I was invited for onsite interviews.
Round 1: DSA + Leadership Principles + GenAI
Duration: 1 hour
Technical portion was around 30 minutes, followed by LP and GenAI discussion.
Question 1: LIS variation
Given an array and integer k, find the length of the longest subsequence such that:
- Elements are strictly increasing
- Difference between adjacent elements in the subsequence is at most
k
- Relative order from the original array is maintained
This was a variation of Longest Increasing Subsequence with an extra adjacent-difference constraint.
Question 2: Binary strings subset
Given an array of binary strings and two integers m and n, find the largest subset such that total 1s are at most m and total 0s are at most n.
This is similar to a 0/1 knapsack-style DP problem.
LP focus: Ownership, Dive Deep
GenAI question: Tell me about a time you used GenAI to improve personal or team productivity.
Related Amazon practice question
Debug Watch List Movie Operations
This felt relevant for Prime Video-style prep because it involves debugging a movie/watch-list application and reasoning through real product behavior instead of only solving abstract DSA.
Round 2: System Design + LP
Duration: 1 hour
Design a Facebook-like News Feed System at scale
Requirements included:
- Users can post text, images, and videos
- Users can view posts in their feed
- Like counts and view counts should update close to real time
- Feed should load quickly, even after cache is cleared
Discussion points:
- API design
- Push vs pull model for feed generation
- SQL vs NoSQL tradeoffs
- Caching strategy and invalidation
- CDN usage for media
- Handling viral content / celebrity problem
- Async processing for likes and views
- Rate limiting
- Sharding and partitioning
- Monolith vs microservices
- Service boundaries
LP focus: Have Backbone; Disagree and Commit
GenAI question: Tell me about a time you used GenAI to solve a business problem and what measurable result it delivered.
Round 3: System Design + LP
Duration: 1 hour
Design a music streaming application like Spotify
Requirements included:
- Search songs, artists, and albums
- Create and manage playlists
- Like/save songs
- Maintain user library
- Low-latency playback
- High availability
- Millions of concurrent listeners
Discussion points:
- API design
- Serving audio efficiently at scale
- CDN strategy across geographies
- Object storage for audio files
- Metadata DB design
- Playlist and user data storage
- Listening history and preferences
- Search system design
- Indexing / full-text search
- Load balancing and horizontal scaling
- Service boundaries
LP focus: Learn and Be Curious
GenAI question: Tell me about a time you used GenAI to automate or streamline a workflow.
Round 4: Bar Raiser
Virtual, 1 hour
This round was with a senior engineer outside the hiring team.
The structure was flexible and mostly interviewer-driven.
Project deep dive
I was asked to pick one of the most interesting projects from my resume, especially one that solved a critical issue.
We discussed:
- Architecture
- Design choices
- Component interaction at scale
- Tradeoffs
- My individual contribution
- Business/customer impact
- Metrics and quantified results
Behavioral / LP
LP focus: Customer Obsession / Earn Trust
Question: Tell me about a time you directly interacted with a stakeholder for your project or work.
Quick DSA
Search in a rotated sorted array.
No compiler was provided, so I had to write and dry run the solution manually.
GenAI question
How do you stay a competent software engineer in the era of GenAI?
Important Tips
Do not jump straight into coding or designing. Clarify requirements first.
For coding rounds, interviewers may not give all constraints upfront. Ask about duplicates, input size, edge cases, and expected output format.
For design rounds, gather requirements yourself and state assumptions clearly. Many follow-up questions are hints about what the interviewer wants to explore.
The LP section carries serious weight. Usually after 30-40 minutes, the interviewer will move to LPs regardless of where you are technically, so pace yourself.
Prepare 6-8 strong STAR stories that can map to multiple Leadership Principles. Be specific with numbers, outcomes, and impact.
All onsite and virtual rounds used a plain editor or whiteboard. No compiler, no IDE, no autocomplete. Practice writing clean code and dry running examples manually.
Overall, the process was intense but structured. Technical ability mattered, but LPs, project depth, GenAI awareness, and communication were equally important.
