Twitch Interview Questions (May 2026)
1 experiences · LeetCode (1)
Top topics
Zepto | SDE 3 | Reject
Interview Experience
Background:
5+ YoE working @ investment bank
Round 1 LLD Round (Interviewvector)
Ques: Design Online book reader (Kindle)
Covered all FR, NFR, Classes, DB schema, API Design and Design Patterns
Interview experience: Panel was easy going, asked some good questions, I was able to answer all
Feedback: Positive
Round 2 HLD Round (Interviewvector)
Ques: Design Streaming service similar to Twitch
Discussed about FR, NFR, Estimation, High level Microservice, Database architecture
Interview experience: Panel was bad at communication but we managed. I was able to answer all follow up questions/deep dives.
Feedback: Positive
Round 3 HLD Round (Zepto EM)
Ques: Design Inventory Management System (for say KFC/McD)
Discussed about FR, NFR, Estimation, Core API\'s, High level Microservice, Database architecture
Interview experience: Good panel, asked some questions on concurrency, transactions & locks. Did some deep dive on MongoDB internal working. He was looking for specific jargons.
Feedback: Positive
Round 4 Hiring Manager Round (Zepto head of eng)
- LLD for WeWork (Only database schema)
- HLD of a project you worked on last 6 months
- Questions on Apache Kafka (current cluster is handling 10m requests per second, how do you scale to 1B)
- Questions on Postgres (internal workings)
- Languages worked with?
- Tell me about a time when a project you worked on went wrong
- Tell me about a time you had to give someone a critical feedback
- How do you make sure you write quality code
- Tell me about a time you learnt something outside of job
- Two things you love and hate about your current job
Interview experience: Panel was arrogant and unresponsive so it was hard to have a two way communication. He was nodding his head at times so I had to be happy with that. Interview went well, answered everything, yet got
rejected.
Feedback:
Rejected
Topics
Twitch Interview Process Overview
The Twitch interview process typically includes a recruiter screen, one to two technical phone screens, and a 4-6 round on-site or virtual on-site loop. Each round serves a distinct calibration purpose: coding rounds measure correctness, code quality, and complexity reasoning; system design rounds measure architectural judgment at the appropriate level; behavioral rounds measure ownership, leadership scope, and collaboration. Reports tagged on LeakCode from 2024-2026 show Twitch runs a calibrated process consistent with industry norms for companies of its tier.
Difficulty calibration: Twitch coding rounds typically run medium difficulty with follow-up depth as the senior discriminator. System design rounds expect production-grade trade-off articulation at L4+ levels. Behavioral rounds expect quantified outcomes ("reduced p99 latency from 800ms to 120ms") rather than vague impact claims. The candidates who advance consistently demonstrate clear thinking out loud rather than perfect final answers.
How To Use Twitch Question Reports
Real candidate-reported interview questions are a calibration tool, not a memorization target. Twitch updates its question pool every 2-4 months; memorizing exact problems risks misleading you when the interviewer uses a variant. The high-leverage approach: identify the patterns that appear repeatedly in Twitch reports, practice those patterns on similar (not identical) problems, and use the reports to understand the interviewer's typical follow-up depth.
Filter the questions above by round type, difficulty, and recency. Focus first on reports from the past 6-12 months; older reports may reference questions that have since rotated out of Twitch's pool. Reports tagged with quantified difficulty and explicit round type are higher-signal than reports without those tags. The metadata filters help you build a focused study plan in 1-2 hours rather than 8-10 hours of unstructured browsing.
Common Twitch Interview Mistakes
Reports tagged "no hire" at Twitch consistently surface a few patterns: jumping into code without clarifying requirements, coding silently for extended periods, missing edge cases (empty input, single element, large input, overflow), producing working code the candidate cannot refactor when probed, and behavioral stories that use "we" instead of "I" diluting individual signal. Strong candidates explicitly avoid these patterns by following a consistent round template.
The single most predictive failure mode in recent reports: not asking clarifying questions. Interviewers are explicitly trained to weight this dimension. Strong candidates ask 3-5 clarifying questions even on problems that look obvious; weak candidates dive into implementation immediately. Strong candidates also verbalize their approach before writing code; weak candidates code in silence and lose the communication dimension of the round's calibration.