ByteDance interview notes, page 7 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 - Behavioral / Leadership

Apr 2026

Behavioral round at Bytedance for a software engineer role, pretty much one big deep-dive into a project you're proud of. They don't let you stay surface-level for long.

Walk me through the most challenging project you've worked on, including your role, what made it hard, the decisions you made, and what the outcome was.

“This sounds like a standard opener but they kept pulling threads.”

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ByteDance·Software Engineer·Onsite - System Design / Architecture

Apr 2026

Bytedance system design round, one big question about building a real-time auction platform. Took up the whole session and went pretty deep into some areas I wasn't fully prepared for.

Design a real-time online auction platform where users can browse auctions, place bids, and see live updates of the current highest bid and bid history. The system needs to handle very high traffic during popular auctions and especially in the final 30 seconds, enforce bid ordering, reject stale or invalid bids, persist all state, and correctly finalize the winner.

“This one sprawled in ways I didn't expect.”

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ByteDance·Software Engineer·Technical Phone Screen

Apr 2026

Bytedance software engineer interview that started with the usual self-intro and resume walkthrough, then went pretty deep on internship specifics before wrapping up with a behavioral question about conflict.

Walk me through the projects on your resume, particularly your internship experiences.
Tell me about a time you disagreed with a manager or teammate. What was the situation, what did you do, and how did it get resolved?

“They didn't just skim the surface.”

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ByteDance·Software Engineer·Technical Phone Screen

Apr 2026

Bytedance SWE interview, coding round, one classic design question that I thought I knew cold but fumbled a bit on the implementation details under pressure.

Design and implement an LRU cache with a fixed capacity that supports get and put operations, both in O(1) average time. get should return -1 if the key isn't found, and put should evict the least recently used entry when the cache is at capacity.

“I knew this problem.”

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ByteDance·Software Engineer·Technical Phone Screen

Apr 2026

Bytedance software engineering interview where the main focus was a deep technical dive into a project from my background. They really wanted to get into the weeds on every decision I'd made.

Walk me through a project you've worked on, including the technical context, why it was built, and the scope of your involvement. Be ready to go deep on design decisions, alternatives you considered, trade-offs, what your specific contribution was versus the team's, what went wrong, and what you'd do differently.

“This is basically one question that unfolds into ten.”

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ByteDance·Software Engineer·Technical Phone Screen

Apr 2026

Bytedance SWE coding round with two back-to-back algorithm problems. The twist was having to write your own test cases and construct the tree yourself, which added some pressure on top of already standard-ish questions.

Given an array of strings, group all strings that are anagrams of each other and return the groups as a list of lists.
Given a binary tree, return the values visible from the right side, ordered top to bottom. You also need to construct the tree and write your own test cases.

“Classic problem but I still fumbled the explanation a bit.”

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

May 2026

Bytedance ML engineer interview with a meaty system design question around recommendation systems. The whole thing was a single deep-dive, no warmup, just straight into the weeds.

In a recommendation system, the recall stage typically retrieves a fixed number of K candidate items to pass to the ranking stage. How would you design a system that determines K dynamically per request instead of using a static value? Cover input features, modeling approach, training signals, and how you'd handle serving constraints and evaluation.

“This one took me a minute to even parse what they were actually asking.”

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ByteDance·Data Scientist·Technical Phone Screen

May 2026

Bytedance data scientist interview that followed the usual flow: intro, resume walkthrough, then a bunch of technical and business questions. Nothing too wild but the bias/variance and metrics questions had some depth to them.

Walk me through your background and resume.
How do you approach data preprocessing for a machine learning pipeline?
How do you handle the bias/variance tradeoff when building a model?
How do you decide which performance metrics to use for a given model?
Given the business context of this role, how would you frame your modeling decisions to align with company goals?

“Pretty standard opener.”

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ByteDance·Software Engineer·Technical Phone Screen

May 2026

Bytedance SWE coding round with a tricky binary string splitting problem. The time pressure was real and from what I gathered most people don't even finish it, which is both reassuring and kind of stressful to know going in.

Given a binary string, split it into exactly 3 non-empty contiguous parts such that each part contains the same number of '1's. Return the number of valid ways to do this, modulo 10^9 + 7.

“The zero case is what gets people.”

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ByteDance·Data Analyst·Technical Phone Screen

May 2026

SQL-heavy data analyst interview at Bytedance, two meaty scenario questions back to back with no warmup. Both were open-ended enough that I kept second-guessing whether I was going too deep or not deep enough.

Revolut has just launched a new paid subscription tier called Ultra. There's no dedicated subscription table, so you have to infer adoption from transactions and activity data. How would you define a successful launch in the first month, and what SQL analyses would you build around it?
Using only the available tables, how would you build a rule-based SQL system to flag suspicious users or transactions for the fraud team? Walk through the rules you'd write, how you'd combine them into a risk score or review queue, and how you'd evaluate whether the system is actually working.

“This one sprawled in a way I wasn't ready for.”

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