TikTok Interview Questions — Page 9

TikTok·Software Engineer·Onsite - Behavioral / Leadership

May 2026

TikTok software engineer interview, one round focused entirely on a deep-dive into a past project you're proud of. About 30 minutes of walking through the whole thing, design choices included, with follow-ups that push you to defend your decisions.

Walk me through a software project you're proud of, covering the problem, your role, the technical design, how you executed it, and what you'd improve.
What alternative approaches did you consider for your design, and why did you go with the one you chose?
How did you validate that your implementation was correct and met the required quality bar?
How did you handle edge cases or failure scenarios in your system?
Looking back, what would you do differently on this project if you had more time or resources?

“This is basically the whole interview.”

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TikTok·Data Scientist·Technical Phone Screen

May 2026

TikTok data scientist interview focused heavily on a binary sentiment classification scenario, walking through the full ML lifecycle from data to deployment. The questions were technical and layered, not the kind you can wing with surface-level answers.

Walk me through your ML pipeline end-to-end for a sentiment classification model, from data sourcing and labeling through training, evaluation, and the practical problems you ran into along the way.
Why did you pick the model or method you used over the alternatives, and what trade-offs does that choice introduce around latency, interpretability, or cost?
How did you iteratively improve the model over time? What did error analysis reveal, and what would you do differently if you started over?
Your model outputs a probability score. How do you pick the decision threshold for deployment, and how does that change with severe class imbalance, asymmetric misclassification costs, or a hard cap on daily review capacity?
A stakeholder wants to define success using a specific metric. How do you evaluate whether that metric is actually the right one, and what data issues like label leakage or sampling bias could make it misleading?

“This one took longer than expected.”

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TikTok·Data Scientist·Technical Phone Screen

May 2026

TikTok data scientist interview that was basically a gauntlet of SQL edge cases. Four parts, all technical, all interconnected across the same schema. The kind of session where you think you're done and then they ask you to redo it a different way.

Using a single grouped query joining orders to users by country, show COUNT(*), COUNT(amount), and COUNT(DISTINCT user_id) side by side. Explain how NULL values affect each count and why the join can cause double-counting issues.
Per country, find the top 2 spenders by total order amount using ROW_NUMBER(). Then redo it with RANK() to handle ties. For country US specifically, which user IDs appear under each method and why do they differ?
Show how filtering orders with amount IS NULL in WHERE versus HAVING produces different results when trying to find users with at least 2 video_play events on a specific date who also have zero non-NULL orders.
Compute average spend per active user (defined as someone with at least one event on a given date) by country, guarding against divide-by-zero and NULL amounts. Explain why you would use COALESCE and NULLIF here and what each one is actually doing.

“I knew COUNT(*) counts all rows including NULLs and COUNT(column) skips them, but I fumbled explaining why COUNT(DISTINCT user_id) matters when users have multiple orders.”

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TikTok·Product Manager·Onsite - Product Sense / Strategy

May 2026

TikTok PM interview focused on a budget allocation case for two ad creatives running mobile game campaigns. The whole thing was essentially one big analytical scenario with several sub-questions layered inside it.

Given performance data for two TikTok ad creatives (impressions, clicks, conversions, revenue, cost), what hypotheses would you form to explain why one creative outperforms the other, in either direction?
Which metric or metrics would you prioritize when comparing the two creatives, and why?
Which campaign would you allocate incremental budget to today, and what's your reasoning?
How do you weigh current ROI against growth headroom and longer-term upside when you're working with a constrained budget?

“I liked this part actually.”

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TikTok·Software Engineer·Technical Phone Screen

May 2026

TikTok software engineering interview that leaned hard into tree reconstruction. One meaty coding problem with a bunch of follow-ups tacked on, felt more like a design conversation than a pure leetcode grind.

Given arrays representing the preorder and inorder traversals of a binary tree with unique values, reconstruct the tree and return its root. Your solution should run in O(n) time and use O(n) extra space.
How would you detect and handle invalid inputs that cannot form a valid binary tree from the given traversal arrays?
Write unit tests for the tree reconstruction function covering typical cases, edge cases, and degenerate cases like an empty tree, a single node, skewed trees, and mismatched arrays.

“The naive recursive approach is easy to sketch out but getting to O(n) requires using a hashmap to look up inorder indices instead of scanning linearly each time.”

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TikTok·Data Scientist·Technical Phone Screen

May 2026

TikTok DS interview, SQL-heavy round focused on their Live product. Three questions total, escalating difficulty, all around the same schema. The hard one at the end was genuinely tricky and I'm not sure I nailed it.

Given a Live sessions table and an activity table, for the first week of October 2022 in the US, how many distinct creators launched more than 3 mobile Live sessions?
For October 2022, find the top 5 creators by total gifts received within each country. Handle ties deterministically.
For yesterday's date, count creators who went live at least twice AND had every single one of their rooms that day watched by 500 or more viewers.

“Pretty straightforward filter-and-count.”

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TikTok·Data Scientist·Technical Phone Screen

May 2026

TikTok data scientist interview with a SQL question focused on random sampling. Pretty straightforward if you know your database functions, but I second-guessed myself more than I should have.

Write a SQL query that randomly selects roughly 10% of rows from a large user table.

“I went with WHERE RAND() < 0.1 which works fine, but then spent like two minutes nervously over-explaining why it's approximate and not exact.”

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TikTok·Data Scientist·Onsite - System Design / Architecture

May 2026

TikTok data scientist interview with a meaty ML system design question about fraud detection. The kind of question that sounds focused until you realize how many moving parts they actually want you to cover.

Design a real-time credit card fraud detection system for an online payments company. Walk through data sources, feature engineering, model selection, real-time architecture, how often you'd retrain, and how you'd catch model drift.

“This question sprawls in every direction and I did not do a great job scoping it before diving in.”

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TikTok·Software Engineer·Technical Phone Screen

May 2026

TikTok coding round for a Software Engineer role. One problem, matrix-based, and the catch was they explicitly told you not to just flatten and sort. That constraint is where things get interesting or, depending on how you think, stressful.

Given an n x m matrix where every row and column is sorted in non-decreasing order, find the k-th smallest element. You must do better than flattening the whole matrix and sorting it.

“The 'do better than full sort' constraint is the whole problem.”

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TikTok·Data Scientist·Technical Phone Screen

May 2026

TikTok data scientist interview with a single monster question covering basically every dimension of experiment design you can think of. The kind of question where you finish answering and still aren't sure if you got half of it right.

You're launching a redesigned onboarding flow expected to lift Day-7 activation and potentially trigger network effects and weekly seasonality. Design a full experiment plan covering: hypothesis and metric definitions with attribution windows, randomization unit and interference handling, sample size and power analysis accounting for seasonality, variance reduction techniques, SRM detection, sequential monitoring and alpha spending, ramp plan with novelty effect controls, diagnostics for noncompliance and bot traffic, spillover detection and bias quantification, and a decision framework when primary and guardrail metrics conflict.

“This one took me a second to process because it's not really one question, it's like ten questions stapled together.”

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