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Pinterest interview notes, page 2 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·Technical Phone Screen

Apr 2026
Pinterest SWE coding round, pretty much a classic graph traversal problem. Nothing too wild but worth knowing your options cold before you walk in.
  • Given a 2D binary grid representing a map of land and water cells, count the number of islands, where an island is a group of land cells connected horizontally or vertically.

“I went with DFS right away which was fine, but I fumbled a bit explaining the space complexity.”

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

Apr 2026
Pinterest ML Engineer interview focused almost entirely on a single deep system design question about content moderation at scale. It was a long, sprawling session and I left unsure whether I'd covered enough ground or just rambled for an hour.
  • Design an ML system to detect unsafe content (violent, sexual, hateful, harassing, or policy-violating posts, images, and videos) on a large user-generated-content platform.

“This thing sprawled in every direction.”

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Pinterest·Software Engineer·Onsite - System Design / Architecture

Apr 2026
Pinterest system design round focused on a graph connectivity problem using pins as nodes. The discussion got pretty deep into tradeoffs between different data structures and query patterns, which I wasn't fully prepared for at that scale.
  • Given a graph where pins are nodes and edges represent shared boards or user interactions, how would you determine if two pins are connected? What data structures would you use, and how does your approach change between one-time queries versus repeated online queries?
  • How would you scale this pin connectivity solution to handle millions of pins and billions of edges? Walk through the architectural decisions you'd make.

“Started with adjacency lists which felt right, then they pushed on union-find and I fumbled explaining the path compression part under pressure.”

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Pinterest·Software Engineer·Technical Phone Screen

Apr 2026
Pinterest phone screen for a software engineering role, one coding question the whole time. Pretty focused on string parsing and knowing when to ask clarifying questions.
  • Given a string of even length containing only digit characters, decode it by reading consecutive pairs where the first character is a repeat count and the second is the digit to repeat. Return the fully expanded string.

“Basically the reverse of the classic count-and-say problem.”

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Pinterest·Software Engineer·Technical Phone Screen

Apr 2026
Pinterest SWE interview, graph question that looked straightforward until the follow-up hit. The core problem was manageable but the cycle handling part is where things got interesting.
  • Given a list of airline tickets where each ticket is a from/to airport pair, reconstruct the full itinerary starting from JFK using every ticket exactly once. If multiple valid orderings exist, return the lexicographically smallest one.
  • Follow-up: how would you handle the case where the graph has cycles, meaning there are multiple repeated edges between the same pair of airports?

“I knew this was Hierholzer's algorithm territory pretty fast, Eulerian path stuff.”

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Pinterest·Data Scientist·Technical Phone Screen

Apr 2026
Pinterest data science interview with a stats-heavy coding question. The whole thing was basically one scenario dressed up as a practical problem, which I actually appreciated more than the usual vague 'tell me about a project' stuff.
  • You have a single array of historical order values from an e-commerce platform. Write efficient Python code to compute a 95% bootstrap confidence interval for the mean using 10,000 resamples, and explain any performance choices you made.

“I knew bootstrapping conceptually but had never actually written it from scratch under pressure.”

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Pinterest·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Pinterest ML Engineer loop had at least one deep theory round that went heavy on loss functions. The breadth they expected was real, covering everything from basic cross-entropy to focal loss and KL divergence, and they clearly wanted you to know the tradeoffs not just the formulas.
  • Walk through the major loss functions used in classification, comparing their formulas, when you'd pick each one, and what can go wrong with each. Expected coverage included binary cross-entropy, categorical cross-entropy, hinge loss, focal loss, label-smoothing, and KL divergence.

“This was the main event and it was a lot.”

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

Apr 2026
Pinterest ML engineer system design round focused entirely on building a search engine for Pins from scratch. Brutal scope: data modeling, indexing, retrieval, ranking, spam, logging, and scaling all in one session. Left feeling like I only got halfway through what they wanted.
  • Design a search engine for Pins. Walk through the full system: data model, indexing pipeline, query understanding, candidate retrieval, ranking, spam and duplicate handling, click logging, A/B testing, and how you'd scale to billions of pins with low latency.
  • How would you build the indexing pipeline to support both text-based and image-based search queries?
  • How would you approach ranking retrieved pin candidates, considering relevance, personalization, freshness, and quality signals together?
  • How would you design the A/B testing framework to evaluate changes to the ranking model?
  • What's your approach to detecting and filtering spam and near-duplicate pins at scale?

“This is basically a full ML platform design crammed into one question.”

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Pinterest·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Pinterest ML Engineer screen, two conceptual questions on training dynamics and backprop. Nothing hands-on, just theory, which I wasn't fully expecting for this role.
  • Why does training loss or accuracy oscillate when the learning rate is set too high, and what happens on the other extreme when it's too low?
  • In a deep fully connected network, are vanishing gradients more of a problem near the input layers or the output layers, and why? What are the standard ways to address it?

“I knew the high-learning-rate answer pretty well: the optimizer overshoots the minimum and bounces around the loss surface.”

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Pinterest·Software Engineer·Technical Phone Screen

Apr 2026
Pinterest coding interview with a graph traversal problem. Pretty standard algorithmic round but the lexicographic ordering constraint is the part that trips people up if you're not careful with how you sort the adjacency list.
  • Given a list of airline tickets as departure-arrival airport pairs, reconstruct the full itinerary using every ticket exactly once, starting from the correct airport. If multiple valid itineraries exist, return the lexicographically smallest one.

“The core of this is Hierholzer's algorithm for Eulerian paths, but I didn't think of it that cleanly in the moment.”

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