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ByteDance interview notes, page 28 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

Jul 2026
Bytedance software engineer interview with a scheduling problem that looks easy until you actually have to implement it clean under pressure.
  • Given an array of meeting time intervals, find the minimum number of conference rooms needed so no two meetings overlap.

“I knew this problem but fumbled the heap approach mid-explanation and ended up describing the two-pointer sort trick instead.”

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ByteDance·Software Engineer·Hiring Manager Screen

Jul 2026
Resume deep-dive round at Bytedance for a software engineering role. Two main areas: walking through a recent promotion and pitching a high-impact project with real numbers. Pretty standard but they pushed hard on trade-offs and metrics.
  • Tell me about a recent promotion or strong performance review. What changed in your scope or expectations, and what specifically did you do to earn it?
  • Walk me through one or two high-impact projects you led. What problem were you solving, what was your specific role, what trade-offs did you make, and what were the measurable results?

“I fumbled the structure a bit here.”

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ByteDance·Frontend Engineer·Technical Phone Screen

Jul 2026
Bytedance frontend screen, one technical question on JavaScript fundamentals. Pretty standard stuff but they went deeper than I expected on the nuances.
  • Can you explain what the rest parameter is in JavaScript, how it works, and how it compares to the arguments object?

“Started okay, got the syntax right (...args as the last parameter), but then I fumbled a bit explaining why you'd use it over arguments.”

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ByteDance·Frontend Engineer·Technical Phone Screen

Jul 2026
Frontend phone screen at Bytedance that basically came down to one classic JavaScript closure question. Nothing crazy, but it's the kind of thing that trips you up if you haven't thought about it recently.
  • Given a for loop that uses setTimeout to log the loop counter, predict the output and explain why it behaves differently with var versus let. Walk through how you'd fix it.

“The var vs let thing I knew, but I fumbled a bit explaining the event loop part.”

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ByteDance·Software Engineer·Technical Phone Screen

Jul 2026
Two coding questions at Bytedance for a software engineer role. The sliding window problem was manageable but the 2D follow-up on the first one threw me off, and the LRU cache is a classic that still requires you to actually implement it cleanly under pressure.
  • Given a binary array and an integer k, find the maximum length of a contiguous subarray of all 1s if you can flip at most k zeros. Follow-up: extend this to a 2D binary matrix and find the largest rectangular submatrix achievable with at most k flips.
  • Design and implement an LRU cache supporting get and put operations, both in O(1) average time.

“The 1D version clicked pretty fast once I thought about maintaining a window with a count of zeros inside it.”

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

Jul 2026
Bytedance ML engineer interview focused almost entirely on LLM agent systems, specifically tool use. The depth they expected was serious, not a surface-level chat about agents.
  • What are the main problems and failure modes you'd expect when building an LLM agent system that relies heavily on tool use in production?
  • How would you model and persist an agent's working state across a long sequence of tool calls?
  • How do you handle context window limits, partial failures, and rollback in a multi-step agent task?
  • What does end-to-end evaluation look like for an agent that takes multi-step actions, and how do you attribute errors to the model versus the tools versus the orchestration layer?
  • What offline benchmarks and online metrics would you use for an LLM agent system, and how do you deal with non-determinism when evaluating it?

“I started listing the obvious stuff like latency and cost, but they pushed back pretty fast and wanted me to go deeper.”

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ByteDance·Software Engineer·Technical Phone Screen

Jul 2026
Bytedance SWE interview, coding round. One algorithm question, pretty standard grid traversal stuff. Nothing unexpected but the pressure is real.
  • Given a 2D grid with open cells and walls, find the shortest path from a start cell to an end cell. Return the path length or -1 if no path exists.

“Classic BFS, four directions, nothing tricky.”

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ByteDance·Data Scientist·Technical Phone Screen

Jul 2026
Bytedance Data Scientist interview with a coding problem on parentheses balancing. Pretty standard algorithmic screen but the problem had a small wrinkle that tripped me up at first.
  • Given a string of only '(' and ')' characters, what is the minimum number of parentheses you need to insert to make the string valid? Walk through your approach and its time and space complexity.

“My first instinct was to just count unmatched brackets with a stack, which got me partway there.”

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ByteDance·Software Engineer·Technical Phone Screen

Jul 2026
Bytedance SRE interview that included at least one coding problem. The question was a classic combinatorics problem and they wanted you to walk through the backtracking logic and complexity, not just produce output.
  • Given two integers n and k, return all possible combinations of k distinct numbers chosen from the range 1 to n. Walk through your approach and analyze the complexity.

“I went straight to backtracking which was the right call, but explaining the pruning step cleanly took me longer than it should have.”

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ByteDance·Product Manager·Onsite - Product Sense / Strategy

Jul 2026
Bytedance PM interview focused entirely on TikTok's content ecosystem: ad experience, moderation pipelines, and AI evaluation. Four meaty strategy questions back to back, no behavioral stuff at all, which I wasn't expecting.
  • How would you compare TikTok's content ecosystem to a competitor's, covering things like supply, demand, monetization, creator incentives, and trust?
  • What changes would you make to TikTok's recommended ads so they feel less intrusive and overly commercial to users?
  • Walk me through how you'd improve the efficiency of TikTok's content moderation pipeline from upload through to appeals.
  • How would you actually measure whether AI is making content moderation better or worse?

“I went with YouTube as the comparison and it worked okay, but I spent too long on discovery mechanics and barely touched governance or creator incentive structures.”

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