Pinterest Interview Questions — Page 23

Pinterest·Software Engineer·Onsite - System Design / Architecture

Jul 2026

Pinterest system design round for a software engineer role. The whole interview was essentially one problem about designing an escape room game state, which sounds fun until you realize they want you to think carefully about leaderboard complexity and tiebreaking logic.

Design a data structure to simulate an escape room game. Player movement and room lookups must be O(1), and the leaderboard must return the top-k players sorted by position descending in O(N + k), with entry time as a tiebreaker for players in the same room.

“I started with the obvious stuff, a hash map from player id to current room, and that covers the O(1) movement and lookup parts no problem.”

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Pinterest·Machine Learning Engineer·Technical Phone Screen

Jul 2026

Pinterest ML Engineer interview that was basically a full coding challenge to implement Bagging with Decision Trees from scratch, no numpy allowed. Pure Python only, which sounds fine until you're rebuilding Gini impurity with nested lists at 11pm.

Implement Bagging (Bootstrap Aggregating) with Decision Trees entirely from scratch in pure Python, no numpy. You need to implement bootstrapping with replacement, fit multiple trees on bootstrap samples, and aggregate predictions via majority vote for classification or mean for regression.
How do you handle the prediction aggregation step in your Bagging implementation, specifically for both classification and regression cases?
What stopping criteria did you implement for your Decision Tree, and why did you choose those specific ones?

“This one took me a while to even scope properly.”

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Pinterest·Software Engineer·Onsite - Coding / Algorithms

Jul 2026

Pinterest coding round, one question the whole time, and it was a string multiplication problem that spiraled into a full algorithms lecture. Not what I expected from a product company but they clearly wanted to see if you knew your theory.

Implement a function that multiplies two non-negative integers represented as decimal strings and returns the product as a string, without converting the inputs to any built-in big-integer type or using an arbitrary-precision library. Handle edge cases like zero inputs, leading zeros, and very large inputs efficiently. Discuss the trade-offs between grade-school O(n*m) multiplication, Karatsuba, and FFT/NTT-based approaches, and state the time and space complexity of your solution.

“I went straight to grade-school long multiplication, which felt right, but I fumbled the carry propagation for a bit and had to restart that part.”

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

Jul 2026

Pinterest ML Engineer interview focused entirely on designing an ad ranking system for a personalized feed. The session was deep and technical, with the interviewer pushing hard on training data construction, bias handling, and embedding strategies. Not a casual screen.

Design an ML system to increase click-through rate for ads shown in a personalized social media feed. Walk through how you'd frame the problem, build and train the model, serve it, and operate it end to end.
What metrics would you track for this system, both online and offline? How do you prevent CTR optimization from degrading user experience or long-term engagement?
How do you construct training data for the CTR model? What are your positive and negative examples, and what logs do you need?
How would you build user and ad embeddings? Describe both a two-tower retrieval approach and a graph-based approach, including what features go into each and how you'd keep them fresh.
How do you handle position bias and selection bias in your training data, and what's your approach to calibration given that the model output feeds directly into auction scoring?
Your model shows strong offline AUC improvements but the A/B test comes back flat or negative. What are the most likely causes and how do you diagnose them?
How would you jointly optimize for CTR and post-click conversions without one signal cannibalizing the other? How does the auction score change in that setup?
A brand-new advertiser campaign launches with zero history. Trace exactly how it gets retrieved, ranked, and given a fair opportunity to accumulate signal without burning through budget.
How do delayed conversions break a naive training pipeline, and what changes would you make to handle them properly?

“This is the main question and it's basically a full system design session disguised as one prompt.”

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Pinterest·Data Scientist·Onsite - Behavioral / Leadership

Jul 2026

Pinterest data scientist loop, behavioral heavy with a technical depth probe baked in. Four main prompts, all requiring concrete numbers and follow-up resilience. The prep bar here is high.

Tell me about a time you disagreed with a product manager's prioritization decision that was based on shaky data. How did you push back and influence the outcome without having direct authority?
Describe a project where success metrics were undefined at the start and requirements kept shifting. How did you settle on a north-star metric, what guardrails did you put around it, and how did you explain your trade-offs to senior leadership?
Walk me through a project where you owned the full analysis from data pull to final recommendation. Be ready for follow-up questions on model assumptions, how you checked data quality, how you handled missing values, and why you didn't just use a simpler approach.
Tell me about a time you ran out of strong examples during an interview or intense questioning session. How did you stay composed, and what did you change going forward?

“This one tripped me up because I kept wanting to frame it as 'I was right, they were wrong' and that reads badly.”

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Pinterest·Data Scientist·Technical Phone Screen

Jul 2026

Pinterest data scientist coding screen, one question the whole time. Pretty focused on pandas manipulation with some edge case gotchas baked in. Left feeling okay about it but not confident.

Given a pandas DataFrame with pin engagement data and a dict mapping category IDs to names, write Python to find the category with the highest average time spent among video pins only. Filter out null category IDs and non-positive time values, break ties by lexicographic order on category name, and return a tuple of (category_name, avg_time) rounded to two decimals. Solution should run in O(n) time with O(k) extra space for k distinct categories.

“I started grouping by category_id right away and almost forgot to filter the nulls before the join with the category_map dict.”

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Pinterest·Data Scientist·Technical Phone Screen

Jul 2026

Pinterest data scientist interview with a system design coding question that was heavier on CS fundamentals than I expected for the role. The thread-safety follow-up is where things got interesting.

Implement an LRU cache with O(1) get and put operations, then walk through how you'd make it thread-safe for concurrent access.

“The data structure part I knew cold.”

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Pinterest·Data Scientist·Technical Phone Screen

Jul 2026

Pinterest data science interview with a pandas-heavy coding question. The problem looked straightforward but had a bunch of data quality landmines buried in it that you had to catch on your own.

Given a DataFrame with user engagement data on pins, filter to video pins (handling case variations and typos like 'vedio'), normalize and map categories using a provided dictionary, exclude invalid time values, and return the canonical category with the highest average time spent along with that average rounded to two decimals.

“The typo thing ('vedio') is the kind of thing you either catch or you don't.”

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Pinterest·Data Scientist·Technical Phone Screen

Jul 2026

Pinterest data science interview with a graph-based coding problem. The question was more algorithmic than I expected for a DS role, felt like it belonged in a software engineering loop.

Given the membership lists of all Pinterest boards, write a function to determine whether two pins are connected through any chain of shared boards. Follow-up: what is the minimum number of boards needed to connect them?

“I stared at this for a bit before realizing it's a graph reachability problem.”

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Pinterest·Data Scientist·Technical Phone Screen

Jul 2026

Pinterest DS interview with a fairly involved SQL question. Single problem but they packed a lot of requirements into it, more than I expected for a phone screen.

Given three tables (events, users, and pin_classification), write a single SQL query to compute click-through rate by pin format for new US users, where 'new' means the user signed up within 30 days before the event date.

“The filtering logic is where I almost tripped up.”

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