TikTok Interview Questions — Page 13

TikTok·Software Engineer·Technical Phone Screen

May 2026

TikTok software engineer coding round, one question the whole time. It was a parsing problem that looks straightforward until you actually try to implement it cleanly under pressure.

Given a string representing a chemical formula (with element symbols, integer counts, and nested parentheses with multipliers), parse it and return the element counts concatenated in lexicographic order. For example, 'K4(ON(SO3)2)3' should return 'K4N3O14S6'. The solution should run in O(n) time.

“I knew immediately it was a stack problem but fumbled the part where you unwind nested parentheses with multipliers.”

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TikTok·Product Manager·Onsite - Product Sense / Strategy

May 2026

PM interview at TikTok with a product sense question about accessibility. Not a lot of context to go on, but it's the kind of question that sounds straightforward until you realize how many directions it can go.

How would you improve Google Maps for users with disabilities?

“I went straight to wheelchair accessibility routing because it felt obvious, then realized halfway through I was basically describing a feature that already exists.”

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TikTok·Data Scientist·Technical Phone Screen

May 2026

TikTok DS interview that was basically a single sprawling case study on paywall experimentation. Dense question, lots of sub-parts, felt more like a take-home prompt squeezed into a live session.

You're launching a new paywall where users are likely to share links with non-paying users. How do you define success metrics and design an experimentation strategy that accounts for interference, variance reduction, power calculations, sequential monitoring, and the final rollout decision?

“This was a lot.”

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TikTok·Data Scientist·Technical Phone Screen

May 2026

TikTok data scientist interview that went deep into generative classifiers. The whole session was basically one long applied stats problem covering LDA, QDA, and regularization. Technically dense and not what I expected from a DS loop.

Given two classes with different means and covariances but assuming equal covariances incorrectly, write the LDA discriminant function and derive the linear decision boundary equation.
Using the true class-specific covariances, write the QDA discriminant and derive the explicit quadratic decision boundary as a scalar equation in x and y.
Classify the point x=(1,1) under both LDA and QDA by numerically evaluating each discriminant. Show your work.
With only 60 samples per class, when would you prefer QDA over LDA, and when is LDA the safer bet? Also, propose a regularized QDA that interpolates between class-specific and pooled covariances using a tunable parameter, and explain how you'd select that parameter.

“This part I actually felt okay about.”

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TikTok·Software Engineer·Technical Phone Screen

May 2026

TikTok data engineering interview that basically dropped a broken Hive query in front of me and said 'fix it.' More of a debugging exercise than anything else, covered a lot of ground fast.

Given a Hive table schema and an INSERT/SELECT statement that fails, walk through all the reasons it might be broken and explain how you'd fix each one.

“This was a lot to hold in your head at once.”

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TikTok·Data Scientist·Onsite - Multi Round

May 2026

Three rounds for a Data Scientist role at TikTok, mixing ML theory, system design, and coding. The questions recycled a few themes (Dropout came up twice across different rounds) which was a little odd, and the coding problems escalated from MinStack to a binary tree path problem that felt more like a software engineering interview than a DS one.

How would you deploy multimodal models when you have limited compute and GPU memory available?
You already have captions and embeddings for a video dataset. How would you speed up video retrieval using those?
What is overfitting and what are the main ways to reduce it?
Implement a MinStack data structure that retrieves the minimum element in O(1) time.
Walk through different normalization techniques (batch norm, layer norm, etc.) and explain how inference behavior differs from training.
How is reinforcement learning from human feedback used in post-training large language models?
Implement a MaxStack that retrieves the maximum element in O(1) time, and then extend it to compute a running median from a data stream.
Explain Dropout again: why does it work, and how does it preserve the distribution of activations at inference time?
Given a binary tree, find whether any upward-only path starting from any node sums to a given target value.

“This one took me a second to organize my thoughts.”

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TikTok·Product Manager·Recruiter / HR Screen

May 2026

Pretty standard PM screen at TikTok, mostly behavioral and self-reflection stuff. Nothing that required deep product thinking, but the TikTok-specific angle on the product usage question kept it from being completely generic.

Walk me through your background and experience.
Do you use TikTok? What do you like or dislike about the product?
Why do you want to be a Product Manager, and why TikTok in particular?
Share three strengths and three weaknesses.
How would the people closest to you describe you?
What's a product you really love and what makes it work?
Tell me about a time you had a disagreement with a coworker or manager and how you handled it.

“Gave my usual spiel.”

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TikTok·Software Engineer·Technical Phone Screen

May 2026

TikTok software engineer interview that was basically a binary tree deep dive. They wanted both a recursive and iterative solution for in-order traversal, plus complexity analysis on the spot. Pretty standard algorithmic round but the dual-implementation requirement kept it from being totally routine.

Given the root of a binary tree, return the in-order traversal of its node values. Implement both a recursive solution and an iterative solution using an explicit stack, then walk through the time and space complexity of each.

“The recursive version came out clean, no issues there.”

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TikTok·Software Engineer·Technical Phone Screen

May 2026

TikTok software engineering interview with a tree problem that looked clean on the surface but had a nasty overflow edge case hiding underneath. One question, but it had enough layers to keep you busy for a while.

Given a binary tree, compute the maximum width across all levels, where width is defined by the span between the leftmost and rightmost nodes using complete binary tree indexing (root=1, left child=2i, right child=2i+1). Implement this in O(n) time, handle potential index overflow for deep trees, and walk through your complexity analysis.

“I got the BFS approach pretty quickly, track the index alongside each node, and at each level just subtract the leftmost index from the rightmost and add one.”

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TikTok·Software Engineer·Technical Phone Screen

May 2026

TikTok SWE interview with a deep-dive performance engineering question. One round, one meaty question, and it basically asked you to reconstruct an entire war story from diagnosis to shipping.

Walk me through a time you had to improve the performance of a live production service. What was the metric you were targeting, how did you find the bottleneck, what did you actually change, and what were the before/after numbers? Also cover the trade-offs you had to navigate and what you took away from it.

“This one is deceptively wide.”

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