Pinterest interview notes, page 1 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·Data Scientist·Technical Phone Screen

Sep 2025

Pinterest DS interview with two meaty SQL problems back to back. The schema was straightforward but the questions layered on enough edge cases that I definitely stumbled on a few details. Worth knowing your window functions and indexing strategy cold before walking in.

Given a schema with daily impressions, users, and countries tables, find the category with the highest total impressions per country for August 2025. Return country name, top category, total impressions for that category, and its share of the country's August impressions rounded to 4 decimal places. Break ties by picking the lexicographically smallest category name.
Using app_events and feature_usage tables, compute a heavy user rate per country for a 7-day window. A heavy user is someone active on at least 4 distinct days in the window AND who used 3 or more distinct features on at least one of those days. Active users are anyone with at least one app_events row in the window. Exclude users with no country mapping from both numerator and denominator.
How would you index the daily_impressions table (with roughly a billion rows) to make both the August category aggregation query and the heavy user rate query performant?

“The three-table join part was fine.”

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Pinterest·Machine Learning Engineer·Technical Phone Screen

Apr 2026

Pinterest ML Engineer interview, technical phone screen focused on model behavior and diagnostics. Pretty standard stuff but they pushed hard on the reasoning behind each mitigation, not just listing them off.

Your model has near-perfect training accuracy but terrible test performance. Walk through why this happens, how you'd diagnose it, and what you'd do to fix it.

“I led with overfitting and the noise-fitting explanation, which landed fine.”

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Pinterest·Software Engineer·Technical Phone Screen

Apr 2026

Pinterest SWE interview that came down to the classic shared expenses / balance settlement problem. Nothing too wild, but the implementation details have more edge cases than you'd expect.

Design and implement a system to track shared expenses among a group of people and compute the minimum set of transactions needed to settle all balances.

“I started with the naive approach, just tracking who owes who after each expense, and it worked fine for the basic case.”

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Pinterest·Machine Learning Engineer·Technical Phone Screen

Apr 2026

Pinterest MLE interview with a probability sampling question that looks straightforward until you start thinking about negative scores and numerical overflow. The kind of problem where the math is the easy part and the implementation details are what actually matter.

Given a list of strings (like user comments) each with a real-valued score that can be negative, sample one string with probability proportional to those scores. How do you handle the mapping and make it numerically stable?

“The part I fumbled on first was the negative scores.”

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Pinterest·Machine Learning Engineer·Technical Phone Screen

Apr 2026

Interviewed for an ML engineer role at Pinterest, pretty standard technical screen with a classic ML concept question.

Can you explain what overfitting is?

“Bread and butter ML question.”

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Pinterest·Software Engineer·Technical Phone Screen

Apr 2026

Phone screen for a Software Engineer role at Pinterest. One algorithmic question, BFS-heavy, the kind that looks manageable until you're actually in it.

Given a list of bus routes where each route is a set of stops, find the minimum number of buses you need to take to travel from a source stop to a destination stop.

“BFS but not on stops, on routes.”

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Pinterest·Machine Learning Engineer·Technical Phone Screen

Apr 2026

Pinterest ML engineer screen that went pretty deep into training dynamics. One question, but it had a lot of layers and I felt like I only half-nailed it.

When is the learning rate most likely to cause oscillations or fluctuations in the training loss curve: when it's too large, too small, or somewhere in between? Walk through your reasoning.

“I led with the too-large case, which was the right call.”

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Pinterest·Software Engineer·Technical Phone Screen

Apr 2026

Pinterest coding screen, robot vacuum problem on a 2D grid. Pretty focused session, just the one problem but they pushed on edge cases more than I expected.

A robot vacuum on a 2D grid can move in 8 directions. Given a single obstacle cell and a starting position, return all cells reachable from the start using BFS or DFS.

“I went with BFS, which felt natural.”

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Pinterest·Machine Learning Engineer·Technical Phone Screen

Apr 2026

Pinterest ML Engineer interview touching on ensemble learning tradeoffs, specifically whether stacking or boosting methods help or hurt interpretability. Short but dense conversation that made me realize I hadn't thought carefully enough about the interpretability angle before walking in.

When using ensemble learning methods, do they improve or worsen model interpretability, and what tradeoffs are involved?

“I went straight to accuracy gains and kind of glossed over the interpretability side, which was clearly the whole point.”

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Pinterest·Machine Learning Engineer·Technical Phone Screen

Apr 2026

Pinterest ML Engineer interview with a coding round that leaned pretty standard on the algorithms side. One question on frequency counting, nothing too exotic, but the follow-up on optimization is where things got interesting.

Given an integer array and an integer k, return the k most frequent elements in the array.

“Started with a hash map to count frequencies, which is fine, but then I went straight for a full sort and they pushed back asking if I could do better.”

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