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TikTok interview notes, page 3 of 28

Large Enterprises

Most of these TikTok write-ups come from Data Scientists (111), then Software Engineers (102) and Product Managers (35). The most reported rounds are the technical phone screen (167), the product sense round (33), the system design round (25) and the behavioral / leadership round (21).

279 interview notes · updated Sep 2026

TikTok·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
TikTok PM interview, one question about metrics for an e-commerce context. Pretty focused session, nothing too wild but it required more structure than I initially brought to it.
  • If you were the PM for an e-commerce store, what metrics would you prioritize and why?

“I went straight to conversion rate and GMV which felt safe but I could tell they wanted me to go deeper.”

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TikTok·Software Engineer·Onsite - Coding / Algorithms

Apr 2026
TikTok software engineering interview with an array manipulation problem that had a tricky follow-up. Pretty standard coding round but the extension question is what separates people who actually understand the pattern from those who just memorized the base case.
  • Given an integer array, count how many indices you can remove so that the sum of elements at even positions equals the sum at odd positions. Then extend your solution to handle removing exactly k elements instead of just one.

“The base case (remove one element) is manageable once you realize that removing an index shifts the parity of everything after it, so you can precompute prefix sums for even and odd positions and check the balance in O(n).”

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TikTok·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
Interviewed for a PM role at TikTok and got a mix of general prioritization questions plus two fairly meaty case-style prompts. The product sense questions were the ones that really separated candidates, I think.
  • How do you decide which products or features to build?
  • How do you prioritize competing requirements and feature requests?
  • Should TikTok launch a feature similar to Instagram Stories? Walk through your reasoning, who it's for, and how you'd measure success.
  • Design a VR app store: what's the goal, who are the core users, what features does it need, and how would you measure whether it's working?

“Pretty open-ended and I rambled a bit at first.”

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

Apr 2026
TikTok ML engineer interview with a system design question about building a monitoring system. Pretty open-ended, which I wasn't fully prepared for.
  • Design a monitoring system for TikTok.

“Way broader than I expected.”

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TikTok·Data Scientist·Onsite - Product Sense / Strategy

Apr 2026
Product sense round for a Data Scientist role at TikTok where they basically handed me a blank canvas and said 'you use TikTok, what's broken, fix it.' Felt open-ended in a way that was either freeing or terrifying depending on how prepared you are.
  • You're a regular TikTok user. What parts of the product frustrate you, and what concrete changes would you make? Walk through the expected impact on users and on the business.

“I went straight to the algorithm and content relevance angle because that felt safest, but looking back I spread myself too thin.”

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TikTok·Data Scientist·Technical Phone Screen

Apr 2026
TikTok data scientist interview with a dynamic programming problem disguised as a feed recommendation scenario. The constraint made it feel applied but it's really just a subsequence optimization with a pairwise condition. Follow-up added repeats with a cap which threw me a bit.
  • Given a list of videos with durations and a user attention span limit A, select a subsequence of videos (preserving order) that maximizes total watch time, subject to the constraint that any two consecutively watched videos have durations summing to at most A. What algorithm do you use and what's the complexity?
  • Follow-up: if the user can rewatch videos, but is limited to watching at most K videos total (including repeats), how do you modify the approach to maximize total watch time under the same consecutive-sum constraint?

“Took me a minute to see this was a DP on subsequences with a pairwise transition constraint.”

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TikTok·Software Engineer·Hiring Manager Screen

Apr 2026
This was a hiring manager screen for a software engineer role at TikTok, basically a long resume walkthrough with some career motivation questions thrown in. Pretty standard stuff but they pushed hard on job changes and salary.
  • Walk me through your resume chronologically, covering your most impactful projects, your specific contributions, the technical decisions you made, measurable outcomes, and what you learned from each role.
  • You've changed jobs fairly frequently. What drove those moves and what were you trying to get out of each transition?
  • What are your career goals and how do you prefer to work day to day?
  • What are your salary expectations and are there any constraints we should know about?

“This took forever and I wasn't prepared for how deep they wanted to go on every single job.”

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TikTok·Data Scientist·Technical Phone Screen

Apr 2026
TikTok data scientist interview with a stats-heavy question about hypothesis testing on a billboard campaign. Pretty much one focused problem but it covered a lot of ground fast.
  • You have a billboard campaign where a sample of 100 users shows a 65% conversion rate. Set up the null and alternative hypotheses for a one-sided test against 60%, then compute the standard error, margin of error at 95% confidence, and the resulting confidence interval. Use the interval to decide whether to reject the null at the 0.05 significance level and explain what that means.

“I knew the formula for SE but fumbled a bit explaining why we use the sample proportion in it rather than the null value.”

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TikTok·Machine Learning Engineer·Technical Phone Screen

Apr 2026
TikTok ML Engineer interview that went pretty deep into the weeds on model diagnostics and transformer internals. Four meaty questions, not a lot of fluff, and they clearly wanted you to go beyond surface-level definitions.
  • How do you diagnose and mitigate overfitting in ML models? Walk through the trade-offs of techniques like regularization, dropout, early stopping, data augmentation, architecture changes, and cross-validation.
  • How do you handle class imbalance? Compare undersampling, oversampling (including SMOTE and its variants), class weighting, focal loss, and threshold adjustment, and explain how you'd evaluate performance under each approach.
  • Walk me through common undersampling strategies, specifically random undersampling, Tomek links, Edited Nearest Neighbors, NearMiss variants, and cluster centroid methods. How does each affect bias, variance, and minority class recall?
  • What is an attention head? Explain queries, keys, and values, how multi-head attention splits the representation space, what different heads tend to capture, and how the number of heads affects model capacity and compute.

“I started with the obvious stuff, L1 vs L2, and the interviewer just nodded and waited.”

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TikTok·Software Engineer·Technical Phone Screen

Apr 2026
TikTok software engineering interview with a Hive SQL debugging question. Pretty technical, felt like they wanted to see a structured thought process more than just a correct answer.
  • You're handed a Hive SQL query that's already written but produces wrong results. Walk through how you'd debug it, fix it, and confirm it's actually correct now. Also cover any performance improvements you'd make.

“This one took me longer than I expected to get into.”

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