Adobe CS 1 | Interview Exp
Summary
I completed five interview rounds at Adobe for a senior backend engineer role, covering DSA, system design, and an AI‑assisted low‑level design, and gained deep insights into scaling, trade‑offs, and emerging interview formats.
Full Experience
Adobe CS1 Interview Experience (5.8 YOE Backend Engineer)
Background:
- 5.8 YOE backend engineer at a large investment bank
- 45 LPA CTC
This was my first interview after college. Recruiter reached out via Instahyre
R1 - DSA
Question:
- Longest Substring with At Most K Distinct Characters https://leetcode.com/problems/longest-substring-with-at-most-k-distinct-characters/
Interviewer joined late and directly jumped to the question. Standard sliding window problem, had solved it before so was able to code/explain it comfortably.
R2 - DSA + Project Discussion
Started with questions around my current project/work.
Coding question:
- Merge Intervals https://leetcode.com/problems/merge-intervals/description/
Solved successfully. Both DSA rounds happened on Hackerrank on the same day.
R3 - Project Deep Dive + HLD (F2F Noida)
This round was heavily focused on past work:
- design decisions
- tradeoffs
- failure handling
- scaling
- concurrency scenarios
Then interviewer asked to design a URL Shortener.
Discussion included:
- unique short URL generation
- DB schema design
- what fields to store and why
- Redis caching
- cache eviction strategy
- scaling bottlenecks
One thing I noticed: interviewer cared much more about reasoning behind decisions than “perfect architecture”.
Overall this round went well and felt like a genuine engineering discussion.
R4 - Hiring Manager + HLD
Again started with deep discussion around my current project and architecture decisions.
Then came:
- “Design YouTube”
This was the toughest round for me. Since I had just started HLD prep, I honestly froze for a few seconds.
I started with basic architecture:
- videos in S3/object storage
- pre‑signed upload/download URLs
- metadata in Postgres/DynamoDB
But interviewer quickly moved to harder parts:
- encoding/transcoding
- adaptive bitrate streaming
- CDN usage
- buffering optimization
- video compression
- handling huge video traffic efficiently
We discussed this for ~30 mins, but I lacked depth in my solution (he was from multimedia domain so he went really deep into stuff). Round didn’t go well.
Biggest learning: For senior backend interviews, interviewers focus a lot on the “hard parts” of distributed systems.
R5 - AI-driven LLD Round
This was a new AI‑assisted round on Hackerrank.
Question:
- Design a Feature Flag Service
Requirements included:
- user/context‑based rollouts
- app‑version targeting
- conditional enable/disable logic, etc.
This round went badly for me.
I approached it like a normal LLD round by discussing entities/classes first. But the interviewer was evaluating:
- how effectively you use AI
- how quickly you iterate
- validating AI‑generated output
- completeness of solution
I should have leveraged AI much earlier instead of trying to manually structure everything upfront.
Final Takeaways
- Know your current project extremely well — most discussions revolved around that
- For HLD, prepare beyond standard diagrams and focus on scaling bottlenecks/tradeoffs
- Interviewers challenge every design decision with “why?”
- AI‑assisted interviews are becoming real, and require separate preparation
- Practicing only DSA is definitely not enough for senior backend roles
Overall a really good learning experience with a strong focus on practical engineering depth.
Interview Questions (5)
Longest Substring with At Most K Distinct Characters
Find the length of the longest substring that contains at most K distinct characters. Typical sliding‑window solution.
Merge Intervals
Given a collection of intervals, merge all overlapping intervals and return an array of the non‑overlapping intervals that cover all the intervals in the input.
URL Shortener Design
Design a URL shortener service. Discuss unique short URL generation, database schema, fields to store, Redis caching, cache eviction strategy, and scaling bottlenecks.
YouTube Design
Design a YouTube‑like platform. Cover video storage in object storage, pre‑signed upload/download URLs, metadata storage, encoding/transcoding pipelines, adaptive bitrate streaming, CDN usage, buffering optimization, video compression, and handling massive traffic.
Feature Flag Service Design
Design a feature flag service that supports user/context based rollouts, app‑version targeting, and conditional enable/disable logic.