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ByteDance interview notes, page 25 of 29

FAANG / Big Tech

Most of these ByteDance write-ups come from Software Engineers (194), then Machine Learning Engineers (45) and Data Scientists (17). The most reported rounds are the technical phone screen (227), the system design round (28), the behavioral / leadership round (16) and the onsite coding round (9).

289 interview notes · updated Sep 2026

ByteDance·Software Engineer·Technical Phone Screen

Jun 2026
Bytedance coding screen, one question the whole time. They threw a constraint I wasn't expecting and it changed everything about how I approached it.
  • Given an m×n grid of '1's (land) and '0's (water), count the number of islands. You must use BFS and cannot modify the grid in place; track visited cells with a separate data structure.

“I've done number of islands before so I felt fine at first, then they said no in-place modification.”

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ByteDance·Software Engineer·Technical Phone Screen

Jun 2026
Bytedance software engineer screen, just one coding problem on linked lists. Pretty standard stuff but they pushed on the space complexity angle which I wasn't fully ready for.
  • Reverse a singly linked list in-place and return the new head. Be prepared to implement both an iterative and a recursive solution and explain the space complexity difference between them.

“The reversal itself wasn't the hard part.”

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ByteDance·Software Engineer·Technical Phone Screen

Jun 2026
Research scientist interview at Bytedance where the main technical portion was a deep dive into your own published work. Pretty research-focused, less leetcode-y than I expected.
  • Walk me through one or two papers from your resume. Cover the research question, what you specifically contributed, the methodology, key results, and where the work could go next. Expect follow-up on technical details, design decisions, and limitations.

“This sounds like a freebie because it's your own work, but it's really not.”

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ByteDance·Software Engineer·Technical Phone Screen

Jun 2026
Bytedance SRE interview that went deep on Linux systems debugging. The whole session basically lived inside one scenario about disk space, but the follow-ups kept branching in ways I wasn't fully prepared for.
  • Walk through your full workflow for troubleshooting a Linux server that's reporting a full disk. Don't just list commands, explain the reasoning behind each step.
  • What do you do when df shows the disk is full but du can't account for the missing space?
  • How would you figure out which specific process is responsible for the disk usage?
  • Explain the difference between inodes and blocks, and how you check each independently on a running system.

“Started with df to confirm which filesystem, then du to find the culprits.”

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ByteDance·Software Engineer·Technical Phone Screen

Jun 2026
Bytedance SRE screen, one coding question the whole time. Pretty standard grid traversal problem but they wanted you to actually talk through tradeoffs between BFS, DFS, and Union-Find which I was not expecting to go that deep on.
  • Given a binary grid where 1s represent land and 0s represent water, count the number of islands. An island is a group of 1-cells connected horizontally or vertically. Walk through your solution and discuss the complexity tradeoffs between different approaches.

“I went straight to DFS because it's the path of least resistance for this kind of problem.”

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ByteDance·Machine Learning Engineer·Onsite - System Design / Architecture

Jun 2026
Bytedance ML engineer system design round, and it did not go well. I went with a recommendation-style framing for a fraud detection problem and the interviewer basically had to redirect me toward graph methods the whole time. Walked out knowing exactly what I should have said.
  • Design a system to detect fake transactions, specifically cases where the same person is both buyer and seller (self-trading).

“My first instinct was to frame it like a recommendation problem, which in hindsight makes no sense for fraud detection.”

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ByteDance·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Bytedance ML engineer interview that went deep on multimodal representation learning. One long technical question covering basically the entire design space of joint embedding models, from data to infra. Dense stuff.
  • Walk through how you would design and train a multimodal large language model to produce joint embeddings across text, image, and audio. Cover data sourcing and alignment, encoder architecture decisions, training objectives and how you'd balance them, negative sampling and curriculum design, how you'd evaluate the resulting embeddings, and scaling and infrastructure considerations.

“This is basically a full system design question disguised as a modeling question.”

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ByteDance·Software Engineer·Technical Phone Screen

Jun 2026
Bytedance SRE interview with a geometry/backtracking coding problem. Pretty niche problem type for an SRE role, not what I was expecting at all.
  • Given an m x n rectangular grid and a list of square side lengths, determine whether all the squares can be cut from the rectangle without overlapping, staying within bounds, and following integer grid lines.

“Took me a minute to even understand what 'cut' meant here.”

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ByteDance·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Bytedance PM interview focused almost entirely on one big monetization strategy question for TikTok's feed. It was a deep, multi-part case that covered experiments, guardrails, creator economics, and long-term trust tradeoffs. Not a quick question you can wing.
  • How would you balance monetization in TikTok's recommendation feed with user experience, so the company can grow revenue without hurting retention, creator health, or long-term ecosystem quality?
  • If increasing ad load boosts revenue but causes a measurable drop in seven-day retention, what do you do?
  • How would you measure and define creator health as a metric in the context of feed monetization?
  • Which user segments might tolerate a higher ad load, and how would you segment your experiments accordingly?
  • What non-advertising monetization levers would you test for TikTok's feed, and how would you prioritize them?
  • How would you detect and measure ad fatigue among users?

“This is a beast of a question.”

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ByteDance·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Bytedance ML Engineer interview for a Commerce Ads role. Started with a short self-intro, then a resume walkthrough that leaned more toward design thinking than technical depth, followed by a focused discussion on measuring seller agent outputs and some caching edge cases.
  • How would you quantify the output results of a seller agent in the context of Commerce Ads?
  • If a user asks two questions that are similar but not identical, how do you make sure the cache returns the right result and doesn't serve a mismatched response?

“This was the main question and it took me a second to find my footing.”

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