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Snap interview notes, page 21 of 22

Large Enterprises

Most of these Snap write-ups come from Software Engineers (117), then Machine Learning Engineers (65) and Data Scientists (18). The most reported rounds are the technical phone screen (149), the system design round (45), the behavioral / leadership round (11) and the product sense round (8).

216 interview notes · updated Jul 2026

Snap·Software Engineer·Technical Phone Screen

Jul 2026
Snapchat SWE interview with a classic graph/celebrity problem. Pretty focused session, one meaty algorithmic question with a follow-up on verification logic.
  • Given n people labeled 0 to n-1, find the 'celebrity' using an API that tells you whether person A knows person B. A celebrity is known by everyone but knows no one. Solve it in O(n) API calls, or return -1 if no celebrity exists.

“The O(n) constraint is what makes this interesting.”

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Snap·Product Manager·Onsite - Product Sense / Strategy

Jul 2026
Snap PM interview with a market expansion question about Indonesia. Pretty open-ended, which I wasn't fully prepared for.
  • How would you approach expanding Snapchat into Indonesia?

“I fumbled the opening by jumping straight into features before even talking about the market.”

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

Jul 2026
Snap ML engineer interview with a system design question around building a healthcare agent for medical history summarization. Pretty open-ended, which I wasn't fully prepared for.
  • How would you design a system for a healthcare agent that summarizes a patient's medical history?

“I went straight to the model layer and spent too long talking about fine-tuning approaches before realizing they probably wanted to hear about data pipelines, privacy constraints, and how you'd handle unstructured clinical notes.”

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Snap·Software Engineer·Technical Phone Screen

Jul 2026
Snapchat SWE interview with a data structures and algorithms question that looked deceptively simple on the surface but had a lot of layers once you got into the query optimization part.
  • Given an unsorted list of timestamp events (with possible duplicates), implement a query(start, end) function that returns the count of events falling within the inclusive time range [start, end].

“My first instinct was to just sort the list and do two binary searches, which gets you most of the way there.”

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Snap·Software Engineer·Technical Phone Screen

Jul 2026
Snapchat software engineer technical phone screen. One graph/BFS-style problem, pretty standard stuff but the exact boolean-only constraint tripped me up a little because I kept wanting to return the path length out of habit.
  • Given a start word, an end word, and a dictionary of valid words, determine whether it's possible to transform the start word into the end word by changing exactly one letter at a time, where every intermediate word must exist in the dictionary. Return true or false.

“My first instinct was BFS and that was correct, but I wasted a few minutes mentally solving for shortest path length before re-reading and realizing they just wanted a boolean.”

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Snap·Software Engineer·Technical Phone Screen

Jul 2026
Snapchat software engineer interview that was pretty much all binary search variations. They pushed hard on both iterative and recursive implementations plus complexity analysis, which I wasn't fully expecting.
  • Given a sorted integer array and a target value, return the index of the first element that is greater than or equal to the target. Implement it both iteratively and recursively, handle duplicates, analyze the time and space complexity, and then explain how you'd modify the approach to find the last element less than or equal to the target.

“The iterative part was fine, lower bound binary search, nothing too bad.”

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Snap·Software Engineer·Technical Phone Screen

Jul 2026
Snapchat software engineer interview with a BFS/graph problem. Pretty standard algorithmic round but the deadend constraint is the part that trips people up if they're not careful.
  • You start with a lock at '0000'. Given a target combination and a list of forbidden states, each turn you can rotate any single digit up or down by one (wrapping 9 to 0 and vice versa). What's the minimum number of moves to reach the target without hitting any forbidden state?

“Classic BFS setup once you see it, but I spent an embarrassing amount of time trying to think about it as a graph problem before realizing it basically IS a graph problem and BFS just falls out naturally.”

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Snap·Machine Learning Engineer·Onsite - Behavioral / Leadership

Jul 2026
Behavioral round for an ML Engineer role at Snapchat, basically one big question about a hard project you led. Pretty standard format but the depth they expected was not.
  • Tell me about a challenging project you led or contributed to. What made it hard, what trade-offs did you navigate, how did you work with stakeholders, and what was the result? What would you do differently?

“This question is deceptively wide.”

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Snap·Machine Learning Engineer·Technical Phone Screen

Jul 2026
Snapchat ML Engineer interview with a coding round that was more of a math-precision trap than a pure DP problem. The question looked like standard coin change until you realized floating-point was the whole point.
  • Given a list of coin denominations as decimal values and a decimal target amount, find the minimum number of coins that sum exactly to the target. Each denomination can be used any number of times. Return -1 if no exact combination exists. You must avoid running DP directly on floats.

“My first instinct was to just throw DP at it with floats and call it a day.”

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Snap·Data Scientist·Technical Phone Screen

Jul 2026
Snapchat data scientist interview that went deep on Random Forest internals. Not a surface-level ML chat, they wanted you to actually know why things work, not just that they work. Pretty rigorous for a single technical round.
  • Walk through every source of randomness in a Random Forest and explain how each one affects model bias and variance.
  • Given a dataset with 100k rows, 100 features, and a 5% positive rate, how would you choose n_estimators, max_depth, and max_features? Specifically explain how max_features controls tree correlation.
  • Compare out-of-bag error to k-fold cross-validation. Under what conditions might they give different results, and why?
  • Why does impurity-based feature importance systematically favor continuous or high-cardinality features? How would you fix it?
  • What strategies exist for handling class imbalance in a Random Forest, and what are the downstream effects on probability calibration and decision thresholds?

“This tripped me up more than I expected.”

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