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TikTok interview notes, page 22 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·Software Engineer·Onsite - Coding / Algorithms

Jun 2026
TikTok software engineering interview with three algorithm problems back to back. Nothing too exotic but the text justification one was more annoying to implement cleanly than I expected.
  • Given an array of words and a max line width, format the words into fully justified text where each line is exactly that width. Spaces should be distributed as evenly as possible, with extra spaces going to the leftmost gaps. The last line is left-justified.
  • Given a string of '(', ')', and '*' characters where '*' can represent either parenthesis or an empty string, determine if there's some assignment of the wildcards that produces a valid balanced sequence. Optionally return one valid assignment.
  • In a 2D grid of open cells and obstacles, find the shortest path between two given coordinates using only up/down/left/right movement. Return the path as a list of coordinates, or an empty list if none exists. Also handle out-of-bounds or blocked start/end cells.

“This one bit me.”

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TikTok·Software Engineer·Onsite - System Design / Architecture

Jun 2026
TikTok SWE interview with a system design question on browser history. Pretty lean on details but the problem itself is a solid one that trips people up if they haven't thought carefully about the data structure side.
  • Design a browser history feature, covering navigation (back, forward, visiting new pages) and the underlying data structure.

“My first instinct was to reach for a linked list and I think that was the right call, but I fumbled explaining why.”

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TikTok·Data Scientist·Technical Phone Screen

Jun 2026
TikTok data science interview that went deep on regression modeling for retention. The whole session was basically one long applied stats problem broken into pieces, and I was not as sharp on the endogeneity section as I should have been.
  • Write out both a linear probability model and a logistic regression for predicting 7-day retention, using features like treatment exposure, day-1 watch time, new user flag, and fixed effects for country and signup date. Include an interaction term between treatment and new user. How do you interpret the treatment coefficient and the interaction term in each model, and for logit, can you convert a coefficient to an odds ratio and then to a marginal effect at the mean?
  • What assumptions and diagnostics would you check for these models? Cover heteroskedasticity, perfect separation, multicollinearity, misspecification, and calibration. Also, what kind of standard errors would you report and how would you decide whether to cluster them, and on what variable?
  • If day-1 watch time is endogenous because it reflects unobserved user preferences, what are two approaches to address that? Walk through the assumptions behind each. What instruments or proxies might actually be valid here?
  • You also have count data for daily videos watched. When would you prefer Poisson or negative binomial regression over OLS for this? How do you check for overdispersion, and how do you interpret the exponentiated coefficients?

“The LPM spec I wrote out fast, no issues.”

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TikTok·Software Engineer·Onsite - System Design / Architecture

Jun 2026
TikTok system design round focused almost entirely on Kafka, and I mean deep Kafka, not the surface-level stuff. It was a long session and they kept pulling the thread on every answer I gave.
  • Walk me through Kafka's core architecture: topics, partitions, leader/follower replicas, replication factor, and in-sync replicas.
  • How do producers decide which partition to send a message to, and how do you configure acknowledgment behavior?
  • Explain how consumer groups manage offset commits and what happens during a rebalance.
  • How does Kafka handle message ordering, and what does enabling idempotent producers actually give you in terms of exactly-once semantics?
  • What strategies do you use to handle backpressure in a Kafka-based pipeline, and how do you tune batch size and compression for throughput?
  • Design a system that guarantees at-least-once delivery end-to-end, then explain what changes to achieve exactly-once semantics.

“I started with topics and partitions fine, but when they pushed on ISR behavior during a leader failure I got a bit fuzzy.”

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TikTok·Software Engineer·Technical Phone Screen

Jun 2026
TikTok software engineer coding round, two algorithmic questions back to back. Both were fairly involved for a single session and the graph one in particular had a lot of moving parts to cover.
  • Given a directed graph with n vertices and m edges, return a valid topological ordering if one exists. If the graph has a cycle, detect it and explain how your approach finds it. Implement both a BFS in-degree approach and a DFS-based approach, compare their time and space complexity, and discuss how you'd handle multiple valid orderings.
  • Given a string text and a shorter string pattern (both lowercase), return all starting indices in text where a substring is a permutation of pattern. Solve it in O(n) time using a sliding window with character frequency counts, and handle edge cases like repeated characters, an empty pattern, or a pattern longer than the text.

“This was a lot to ask in one question.”

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TikTok·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026
Interviewed for a Software Engineer role at TikTok and the process had some real logistics problems worth talking about. The question I got was less about code and more about how you handle chaos, which I wasn't expecting.
  • Tell me about a time an interview process or project coordination broke down due to disorganization. How did you handle the logistics, keep everyone on the same page, and what would you do differently to prevent it?

“This tripped me up a bit because I kept trying to make my answer sound impressive instead of just being specific.”

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TikTok·Data Scientist·Technical Phone Screen

Jun 2026
TikTok data scientist interview with a heavy stats/ML modeling question that felt more like a take-home problem compressed into a live session. One question, very deep, and they clearly wanted to see if you could reason through model selection end to end rather than just recite definitions.
  • You have 100k rows with features x1 (ranging 0 to 100), x2, x3, and a target y. The true data-generating process is piecewise linear with a breakpoint at x1=50, an interaction between x2 and x3, and heteroskedastic noise that grows with x1. Walk through how you'd decide between linear regression and a decision tree: what feature engineering and linearity tests you'd run, how you'd check for heteroskedasticity in residuals, how you'd set up a fair model comparison, how you'd enforce monotonicity or interaction constraints in a tree model, and which model you'd expect to generalize better and why using bias-variance reasoning and learning curves.

“This one took me a second to even parse.”

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TikTok·Data Scientist·Technical Phone Screen

Jun 2026
Got a pretty brutal product experimentation question at TikTok for a DS role. The whole thing was a multi-part case study about designing an A/B test for a bike delivery opt-in program, and it went way deeper than I expected for a single question.
  • A delivery platform wants to let existing car dashers opt into using their own bikes or e-bikes for deliveries while keeping car access. You need to decide whether to scale this program next quarter. Walk through the full experiment design: business goals and a falsifiable primary hypothesis, success and guardrail metrics with exact definitions, randomization unit choice and justification, power analysis inputs, pre-rollout instrumentation, analysis plan for heterogeneous effects and selection bias, and a decision framework for scale-up vs rollback.

“This thing had seven sub-parts and I definitely didn't pace myself well.”

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TikTok·Software Engineer·Technical Phone Screen

Jun 2026
TikTok software engineer coding round, one algorithmic problem the whole session. Pretty standard selection algorithm question but the complexity analysis at the end is where things got interesting.
  • Given an unsorted array of integers and a positive integer k, find the k-th largest element. Walk through your algorithm design, write working code, and analyze both average-case time and space complexity.

“I jumped straight to quickselect without much preamble, which I think was the right call.”

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TikTok·Data Scientist·Technical Phone Screen

Jun 2026
TikTok data scientist SQL round, one question but with enough follow-ups to keep you on your toes for a while. Pretty standard window function territory but the edge case discussion got interesting.
  • Write a SQL query to return the top 1 highest-paid employee per department, given a table that may have multiple rows per employee per department due to historical salary changes.

“My first instinct was ROW_NUMBER() partitioned by department, ordered by salary descending, which is correct.”

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