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TikTok interview notes, page 19 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·Machine Learning Engineer·Technical Phone Screen

Jun 2026
TikTok ML engineer round, basically one meaty algorithm problem that took up the whole session. The follow-up discussion on pruning and worst-case inputs was where things got interesting.
  • Given a sorted array of distinct integers representing stone positions in a river (starting at 0), determine whether a frog can reach the last stone. The frog starts at position 0, its first jump must be exactly 1 unit, and each subsequent jump can be k-1, k, or k+1 units where k was the previous jump length (and must be greater than 0). Design an efficient algorithm, justify its correctness, and analyze time and space complexity. Also discuss pruning strategies and what inputs would hit worst-case behavior.

“I went straight to DP with a hash map keyed on stone index storing the set of jump sizes that can land there.”

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TikTok·Software Engineer·Onsite - System Design / Architecture

Jun 2026
TikTok system design round for a software engineering role. The whole thing was one big data modeling question with a lot of moving parts, and I left feeling like I'd covered maybe 70% of what they were actually looking for.
  • Given a business scenario and a set of required metrics, design a full data model: define your tables, each table's grain, column names and data types, nullability, primary and foreign keys, partitioning and indexing strategy, and explain how each metric gets computed from your schema. Also discuss trade-offs like star vs. snowflake schema, denormalization decisions, slowly changing dimension handling, and data quality concerns.

“This was one question but it was basically five questions stitched together.”

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TikTok·Data Scientist·Technical Phone Screen

Jun 2026
TikTok data science interview focused on ads product strategy, specifically the tension between growing DAU and squeezing more ad revenue. One meaty case question that required pulling together a bunch of different analytical angles at once.
  • How would you analyze the trade-off between growing daily active users and maximizing ad revenue in an ads product? Walk through the metrics, experiment design, and business considerations you'd use.

“I started with ARPU and retention which felt right, but I underweighted ad-load elasticity early on and the interviewer kept nudging me toward it.”

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TikTok·Data Scientist·Onsite - Product Sense / Strategy

Jun 2026
A TikTok data scientist case question that was basically a full product launch framework crammed into one prompt. Walked out feeling like I'd just taken a three-hour strategy exam in 45 minutes.
  • TikTok is entering a new market in Q4-2025. Rank these four factors by importance for a go/no-go decision and rollout plan, justify your ranking, and explain how you'd measure each factor before launch: regulatory and platform constraints, creator supply and local content fit, user acquisition cost and expected retention, and advertiser demand and payments readiness.
  • For each of those four factors, define quantifiable proxies and target thresholds, and explain how you'd actually obtain that data before the market launch.
  • Design a 4 to 6 week staged test for this market entry, including something like a smoke test or waitlist, creator seeding, and a limited beta. What are your success metrics and stop/go/expand criteria at each stage?
  • Build a simple scoring model with weights and data sources to compare this target market against two nearby markets. How would you handle ties?
  • What are the top three risks in the first 90 days post-launch and what concrete mitigations would you put in place for each?

“I started with regulatory because a hard legal block kills everything else, so that's non-negotiable at the top.”

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TikTok·Software Engineer·Technical Phone Screen

Jun 2026
TikTok software engineer interview with two algorithmic problems. The first was a meaty array question with multiple required approaches, and the second was a string rotation variant with a twist on returning move counts. Pretty standard coding round but they pushed hard on complexity tradeoffs.
  • Given an unsorted array of integers and a number k, return the k largest elements in descending order. You need to implement at least two approaches, talk through time and space complexity for each, and explain how you'd handle duplicates and very large inputs like streaming data.
  • Given two strings s and goal, you can repeatedly move the first character of s to the end. Can s be transformed into goal this way, and if so what is the minimum number of moves required? Return -1 if it's not possible.

“This one took longer than I expected because they actually wanted two working implementations, not just a description.”

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TikTok·Data Scientist·Onsite - System Design / Architecture

Jun 2026
TikTok DS interview that went deep into recommendation system design, specifically around balancing user growth against creator monetization for long-form content. It was a single meaty question with a lot of sub-parts and I felt like I was scrambling to cover all of them coherently.
  • Design a recommendations optimization objective that balances user growth and creator monetization for long-form content. How would you structure the objective function, estimate per-impression value, and enforce constraints like latency, diversity, and fairness to new creators?
  • What offline metrics would you define for this system, how would you correlate them with online A/B results, and what's your plan to mitigate training-serving skew?
  • How would you handle cold-start for both new users and new creators, including feature design, priors, and exploration strategies, while avoiding a revenue cliff from too much exploration?
  • How would you manage interference between the recommendation ranker and monetization ad surfaces, and how would you attribute incremental revenue specifically to the recommender versus other surfaces?

“This is where I spent the most time and probably got the most tangled.”

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TikTok·Data Scientist·Technical Phone Screen

Jun 2026
TikTok Data Scientist interview had a simulation-based question that felt more like an operations research puzzle than anything I'd prepped for. The car-building scheduling scenario threw me off a bit since I was expecting standard stats or SQL stuff.
  • You're given a manufacturing pipeline with three sequential stations (chassis, engine, paint) and limited buffer capacity between them. How would you maximize the number of completed units within a fixed 5-minute simulation window?

“I went straight to bottleneck analysis, which felt right, but I fumbled when they pushed on what happens when the middle buffer fills up and the upstream station has to stall.”

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TikTok·Data Scientist·Technical Phone Screen

Jun 2026
SQL-heavy technical screen for a Data Scientist role at TikTok. The main problem was about aggregating buggy vs non-buggy submission counts per employer using conditional aggregation, with a few follow-up angles thrown in.
  • Write a SQL query that counts buggy and non-buggy submissions for each employer, including employers with zero submissions, ordered by employer_id. Use conditional aggregation in a single query.
  • How would you adapt the query if the submission status column were a string like 'buggy' or 'non_buggy' instead of a boolean?
  • If a new employer is added with no submissions at all, does your query still return a row for them with zero counts?

“The LEFT JOIN part I got right away, that's pretty standard.”

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TikTok·Software Engineer·Technical Phone Screen

Jun 2026
TikTok software engineering interview that went deep on graph algorithms. One question, but it had a lot of layers and they really pushed on the implementation details and complexity analysis.
  • Given a set of pipelines and directed dependency pairs, return a valid execution order satisfying all dependencies, or detect if a cycle makes that impossible. Walk through your algorithm, data structures, time and space complexity, and implement both a DFS-based topological sort and Kahn's algorithm. Also discuss tie-breaking for independent pipelines and scaling to very large graphs.

“This started fine.”

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TikTok·Data Scientist·Technical Phone Screen

Jun 2026
SQL-heavy technical screen for a Data Scientist role at TikTok. The main problem was a product catalog question requiring both a window function version and a subquery version, plus a discussion of NULL handling and numeric precision. Felt like a mid-level data engineering question more than a pure DS one, which threw me off a bit.
  • Given a products table with list_price and a nullable sale_price, write a SQL query that returns exactly one product per category: the one with the highest absolute discount (defined as max(list_price minus COALESCE(sale_price, list_price), 0)), breaking ties by smallest product_id. Return category, product_id, list_price, sale_price, and discount_amount.
  • Walk through how your query handles NULL sale_price values and avoids floating point precision issues with the discount calculation.

“This is the core question and it has a few layers.”

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