ByteDance interview notes, page 10 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·Onsite - Coding / Algorithms

May 2026

Bytedance SRE coding round with three array problems back to back. Nothing too wild but the carry propagation one tripped me up more than I expected.

Given a sorted integer array and a target value, return the index of the target using an O(log n) algorithm. Return -1 if not found.
You're given an array of digits representing a nonnegative integer. Add 1 to it and return the result as a digit array, without converting the whole thing to a native integer type.
Rearrange an integer array in place so all nonzero elements come first in their original order, with zeros pushed to the end. O(1) extra space required.

“Binary search, pretty standard.”

View Post

ByteDance·Software Engineer·Technical Phone Screen

May 2026

Bytedance SWE interview with a graph algorithm question. Pretty focused session, just the one problem but they wanted a clean implementation.

Given a DAG, print the execution order of its nodes using topological sort.

“I went with Kahn's algorithm (BFS-based with in-degree tracking) because I always fumble the DFS post-order version under pressure.”

View Post

ByteDance·Software Engineer·Technical Phone Screen

May 2026

Bytedance SRE interview that went heavier on algorithms than I expected. They had me implement N-Queens from scratch and then walk through the complexity analysis, which felt more like a software engineering screen than anything SRE-specific.

Given an integer n, write a function that returns all valid ways to place n queens on an n x n chessboard such that no two queens threaten each other. Return each valid board as a list of strings. Then analyze the time and space complexity of your solution.

“Knew it was backtracking pretty quick but the implementation took me longer than I'd like to admit.”

View Post

ByteDance·Machine Learning Engineer·Technical Phone Screen

May 2026

Bytedance ML engineer interview that got into some solid evaluation metric theory. The ROC/AUC question was the centerpiece and required more nuance than I initially expected.

Explain the ROC curve and AUC for binary classification, including what they measure, why AUC is useful across different thresholds and class distributions, and when you might prefer a different metric instead.

“I started okay, got the TPR vs FPR plot explained fine, but then fumbled a bit on the probabilistic interpretation of AUC.”

View Post

ByteDance·Software Engineer·Technical Phone Screen

May 2026

Bytedance SWE interview with a classic bracket validation problem. Pretty standard algorithmic round, nothing too surprising, but the stack constraint made it feel more intentional than a throwaway warmup.

Given a string made up only of parentheses, curly braces, and square brackets, write a function to determine if the string is valid. Valid means every opening bracket is closed by the matching type, and they close in the correct nested order. You must solve it using a stack in O(n) time.

“I knew this one pretty quickly but still managed to fumble the edge cases early on.”

View Post

ByteDance·Software Engineer·Onsite - System Design / Architecture

May 2026

Bytedance system design round that started with a coding warmup on URL shortening and then spiraled into a pretty deep architectural discussion. More ground covered than I expected for a single session.

Implement a URL-shortening class with a shorten(long_url) method and an expand(short_url) method.
What are the different approaches to generating the short code, and what are the trade-offs between them?
How would you design the storage schema for a URL shortener, and how do you scale both reads and writes?
Should the same long URL always return the same short URL, or is it acceptable to generate a new one each time? How do you enforce whichever policy you choose?
How would you handle caching, click count analytics, link expiration, and abuse or safety concerns in a URL shortener?

“Straightforward enough on the surface.”

View Post

ByteDance·Backend Engineer·Technical Phone Screen

May 2026

Bytedance backend interview, got asked about AI/agent tooling concepts which I wasn't expecting to come up in a backend screen. Just the one question here but it was a bit of a curveball if you haven't been following the LLM ecosystem closely.

What is the difference between MCP (Model Context Protocol) and a Skill in the context of AI agent systems?

“Blanked for a second because I'd seen MCP mentioned but never really sat down to think through how it contrasts with the skill abstraction.”

View Post

ByteDance·Backend Engineer·Onsite - System Design / Architecture

May 2026

Bytedance backend interview that was basically a deep dive on Kafka. One question, but they really meant it.

Walk me through how Apache Kafka works, including its architecture, how producers and consumers interact with it, how offsets and consumer groups function, what delivery guarantees it provides, and where you'd actually use it.

“This sounds like a broad question but they pushed hard on specifics.”

View Post

ByteDance·Backend Engineer·Technical Phone Screen

May 2026

Bytedance backend engineer interview with a coding round. Just the one question from what I remember, pretty standard stuff.

Given a string containing various bracket types, determine whether the parentheses are valid and properly matched.

“Classic stack problem, got through it fine.”

View Post

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

May 2026

System design round at Bytedance for an MLE role, focused entirely on building a production-grade LLM agent that orchestrates external tools across complex business workflows. Pretty deep question with a lot of surface area to cover.

Design an enterprise LLM agent that can use external tools (document retrieval, search, SQL queries, ticketing systems, messaging APIs, workflow services) to complete multi-step business tasks.
What are the major failure modes when tool-using agents are deployed in real production environments?
How would you represent and persist complex state across long-running, multi-turn, and potentially branching agent workflows?
How would you evaluate the quality, reliability, safety, and business usefulness of this kind of agent system, both offline and in production?

“The scope of this hit me fast.”

View Post