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Pinterest interview notes, page 7 of 24

Large Enterprises

Most of these Pinterest write-ups come from Machine Learning Engineers (91), then Software Engineers (77) and Data Scientists (51). The most reported rounds are the technical phone screen (168), the system design round (37), the product sense round (14) and the behavioral / leadership round (5).

235 interview notes · updated Sep 2026

Pinterest·Software Engineer·Onsite - Coding / Algorithms

May 2026
Pinterest SWE coding round, three problems ranging from easy warmup to a genuinely tricky streaming design question. The set equality problem was a freebie but the sliding window ad ranking one had some teeth to it.
  • Given two integer arrays that may contain duplicates and aren't sorted, determine whether they represent the same set of unique elements.
  • Design a system to find the top K ads by impression count within a sliding time window, first as a batch query over a sorted event log, then as a streaming data structure supporting ingest and query operations.
  • Can a target string be formed using characters from a source string, where each character in the source can only be used once?

“Easiest of the three.”

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Pinterest·Product Manager·Onsite - Product Sense / Strategy

May 2026
Pinterest PM interview with a product design question around search and discovery. Pretty open-ended, which I wasn't fully prepared for.
  • Design a feature that helps users locate items on Pinterest.

“I jumped straight into a visual search angle because Pinterest feels like a visual-first product, but I think I underweighted the intent side of things.”

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Pinterest·Software Engineer·Technical Phone Screen

May 2026
Pinterest SWE interview with a graph/DFS problem that looks straightforward until you actually try to implement it correctly under pressure.
  • Given a list of airline tickets where each ticket is a [from, to] pair, reconstruct the full itinerary starting from JFK. Use every ticket exactly once, and if multiple valid orderings exist, return the one that comes first lexicographically.

“I knew this was Eulerian path territory but my first instinct was plain DFS and I didn't think carefully about the lexical ordering constraint until I was mid-implementation.”

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Pinterest·Machine Learning Engineer·Technical Phone Screen

May 2026
Pinterest MLE interview that centered on a trie-based autocomplete system. Pretty implementation-heavy for a machine learning role, but they clearly cared about whether you actually understood data structures under the hood.
  • Design and implement a search autocomplete service backed by a trie. It should support inserting words with optional weights or frequencies, and given a prefix, return the top-k completions ranked by weight.

“The core implementation wasn't the hard part.”

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Pinterest·Software Engineer·Technical Phone Screen

May 2026
Pinterest system design phone screen for a software engineering role. The whole thing was one meaty design problem about a quiz-game ranking system, and most of the time was spent debating data structure trade-offs rather than writing actual code.
  • Design and implement an object-oriented quiz-game ranking system with n rooms and m players. Everyone starts in room 0 and moves forward by answering correctly. Support three operations: getTop(k) returns the top-k players ranked by room number (ties broken by who arrived earlier), proceedToNextRoom(personId) advances a player, and getPeople(roomNumber) returns how many players are in a given room.

“I went straight to a sorted set keyed by negative room and arrival sequence, which handles getTop cleanly but I fumbled explaining why that makes proceedToNextRoom more expensive since you're doing a delete and reinsert.”

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Pinterest·Software Engineer·Onsite - System Design / Architecture

May 2026
Pinterest system design round focused entirely on building a typeahead/autocomplete system at scale. Pretty deep dive, they wanted specifics on basically every layer of the stack.
  • Design a large-scale typeahead/search autocomplete system that returns the top-K most relevant suggestions per keystroke with very low latency.
  • How would you rank autocomplete suggestions, and what signals would you incorporate beyond raw query popularity?
  • Walk me through how you'd build and continuously refresh the suggestion index from query logs.
  • How would you design the caching strategy across edge, client, and service tiers for this system?
  • How would you shard the suggestion index: by prefix or by user?
  • How would you handle typos and fuzzy matching in the autocomplete system without killing latency?
  • How does your design scale to billions of queries per day?

“This is the main question and it sprawls fast.”

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Pinterest·Software Engineer·Technical Phone Screen

May 2026
Pinterest SWE interview with a grid pathfinding problem that looks like a classic BFS until you realize you have to track which keys you've picked up. Took me a bit to get the state space right and the follow-up questions on correctness and edge cases kept coming.
  • You have a 2D grid of rooms where some cells are walls, some are open, some contain keys (lowercase letters), and some are locked doors (matching uppercase letters). Starting from a given cell, find the minimum number of steps to reach the exit. You can move in four directions, can't pass through a door without its key, and can revisit cells.
  • Why is BFS the right algorithm here, and how does it guarantee the minimum number of steps?
  • What are the edge cases your solution needs to handle for this problem?

“My first instinct was plain BFS and I started coding it before realizing state is wrong if you don't track which keys you have.”

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Pinterest·Machine Learning Engineer·Technical Phone Screen

May 2026
Pinterest ML engineer interview with a gradient descent coding question. Pretty focused on fundamentals, nothing too wild, but you need to actually know your math.
  • Complete the missing portions of a gradient descent implementation in code.

“This is one of those questions where you think you know it until you're staring at a half-written function.”

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Pinterest·Machine Learning Engineer·Technical Phone Screen

May 2026
Pinterest ML Engineer interview with a string multiplication problem that sounds easy until you remember you can't just cast to int and multiply. Straightforward round but the constraint is the whole point.
  • Given two non-negative integers represented as strings, return their product also as a string. You cannot use any BigInteger library or convert the inputs to integers directly.

“The constraint is what makes this annoying.”

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Pinterest·Software Engineer·Technical Phone Screen

May 2026
Phone screen for a software engineer role at Pinterest, roughly an hour long. The main coding problem was manageable since I'd seen it before, but a follow-up variant tripped me up and I think that's what sank it.
  • Solve LeetCode 1564 (Put Boxes Into the Warehouse I).
  • Follow-up: given the same warehouse problem, how would you maximize the total height of all boxes placed, rather than just the count?

“Had actually drilled this one before the call so it went fine.”

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