Pinterest Interview Questions — Page 14

Pinterest·Software Engineer·Onsite - System Design / Architecture

Jun 2026

Pinterest system design round, one big question about building a blob storage service from scratch. Pretty intense scope, they wanted end-to-end coverage across a lot of dimensions and weren't shy about pushing on trade-offs.

Design a large-scale, highly available blob storage service similar to Amazon S3, covering APIs, data model, architecture, partitioning, durability, consistency, large object handling, security, and monitoring.

“This one sprawls in every direction and I felt it immediately.”

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Pinterest·Data Scientist·Technical Phone Screen

Jun 2026

Pinterest DS interview with a SQL-heavy technical screen. Two questions, both revolving around the same three tables, but the second one had enough moving parts that I was scribbling CTEs in my head the whole time they were still reading the prompt.

Given an impressions table with user, country, and category data, write SQL to return, for each country, the content category with the highest total impressions and its count.
Using the same dataset, compute the weekly heavy-user rate, where a heavy user is defined as someone active on at least 4 distinct days in the last 7 days AND who used 3 or more distinct features on at least one of those active days.

“This part felt manageable.”

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Pinterest·Data Scientist·Technical Phone Screen

Jun 2026

Pinterest DS interview with a single massive experiment design question covering metric selection, experiment design, power analysis, and decision-making all rolled into one. The depth required was pretty intense for what I expected to be a standard A/B testing discussion.

You're launching a new Shopping module embedded in the Pins feed. Design a full experiment and metric plan: choose a primary success metric and 3-5 guardrail metrics with precise definitions (numerator, denominator, unit of analysis, aggregation window), including at least one funnel metric. Discuss the tradeoffs of DAU and time spent as primaries versus alternatives like Shopping CTR, Add-to-Cart Rate, or GMV per user.
How would you handle spillover and interference in this experiment, given that repins and shares could expose control users to the Shopping module? Propose and justify a concrete randomization design, covering the randomization unit, eligibility rules, cooldown period, novelty burn-in, and how you'd detect and quantify spillover and learning effects.
Walk through your power and MDE calculations for this experiment: what baseline rate assumptions would you make, how do you account for intra-cluster correlation if clustered, how long would you run it, and how would you handle weekly seasonality and peaky traffic? Also describe your AA test plan and any covariate adjustment strategy.
After 21 days you see +2.3% in Shopping CTR and +1.1% in GMV per user, but -0.6% in overall time spent and -0.2% in DAU. How do you build a decision framework for this result? Define your guardrail thresholds, describe how you'd weight these metrics against each other, account for potential long-term effects, and give a concrete recommendation to the PM. What additional diagnostics would you run before any rollout decision?

“The part that tripped me up was being asked to define metrics with numerator/denominator precision rather than just naming them.”

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Pinterest·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026

Behavioral round for a software engineer role at Pinterest. Pretty standard stuff but they go deep on the interpersonal side, more than I expected for an eng interview.

Tell me about a time you had a conflict with a teammate and how you resolved it.
Describe a failure you experienced and what you took away from it.
How do you handle unclear or shifting requirements on a project?
Why do you want to work at this company and on this team specifically?
How do you approach giving and receiving feedback?
Walk me through a time you improved a process or helped grow someone else on the team.

“I had a story ready but fumbled the resolution part.”

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Pinterest·Data Scientist·Technical Phone Screen

Jun 2026

Pinterest DS interview focused almost entirely on a single dense A/B testing case study about video pins in the home feed. Five sub-questions stacked on top of each other, covering everything from hypothesis setup to SRM checks to multiple comparison corrections. Felt more like a take-home problem being walked through live than a conversational interview.

State the null and alternative hypotheses for the primary metric precisely, including the direction of the test and the minimum detectable effect.
Run a chi-square test to check for sample ratio mismatch given the observed assignment counts, and explain whether SRM should be suspected.
For the primary metric, compute the absolute lift, relative lift, a 95% confidence interval for the difference in means, and the one-sided p-value. Is it significant at alpha = 0.05?
Run two-proportion z-tests for CTR and D7 retention as secondary metrics, then apply Holm-Bonferroni correction at a familywise alpha of 0.05. Which metrics remain significant after correction?
Give a ship or no-ship recommendation. How do SRM, novelty effects, and other validity concerns affect your decision, and what additional checks would you run before launching?

“I fumbled this a little by writing H1 as just 'Treatment > Control' without anchoring it to the MDE.”

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Pinterest·Data Analyst·Technical Phone Screen

Jun 2026

Pinterest data analyst interview, one SQL question focused on cancellation behavior in a scheduling product. Pretty technical for an analyst role, more like a data engineering problem than the usual metrics stuff.

Given an appointments table with user IDs, scheduled timestamps, and a status field, write a SQL query to find the percentage of users whose first cancelled appointment was never followed by a returned or confirmed appointment (strictly after, same timestamp doesn't count).

“The 'strictly greater than' part is where I almost tripped up.”

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Pinterest·Software Engineer·Onsite - System Design / Architecture

Jun 2026

Pinterest system design round, two questions in one session. Started with a rate limiter and then pivoted into an elevator dispatch problem that got pretty involved with data structures.

Design a rate limiter system.
Given a set of incoming elevator requests each with a timestamp, design a system to assign each request to the nearest available elevator, where each elevator moves at one floor per second. How would you dynamically maintain a data structure to efficiently find the nearest elevator at any point in time?

“Pretty standard as system design questions go.”

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Pinterest·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Pinterest ML Engineer interview with a from-scratch Naive Bayes implementation question. Pretty focused on the math and numerical stability side of things, not just slapping sklearn on it.

Implement a Naive Bayes classifier from scratch using NumPy. Your implementation should have a fit method to estimate class priors and likelihood parameters, and a predict method to compute posteriors and return the most likely class. You also need to specify which variant you're implementing (Gaussian, Multinomial, or Bernoulli) and handle numerical stability.

“I went with Gaussian NB since the prompt mentioned continuous features and that felt like the safest bet.”

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Pinterest·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Pinterest MLE interview that went pretty deep on data structures, specifically a full trie implementation with some follow-up discussion on memory tradeoffs and deletion edge cases. Not what I expected for an ML role but apparently they care about fundamentals.

Implement a Trie (prefix tree) with add, search, and delete operations, each running in O(length of input string) time. For delete, you also need to free any internal nodes that are no longer needed by any other word.
What are the tradeoffs between storing trie children as a hash map versus a fixed-size array?
How would you handle concurrent reads and writes to this trie in a multi-threaded environment?

“The add and search parts were fine, I've done those before.”

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Pinterest·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Pinterest ML Engineer screen, one coding question that looked simple on the surface but had a bunch of edge cases stacked underneath it. The constraint of not using float() is what makes it interesting and also where most people probably trip up.

Implement a round() function that takes a numeric string and returns the rounded result without using float() for parsing. Handle edge cases like leading signs, implicit zeros, very large numbers, and malformed input. Return the result as a string.

“The no-float() constraint is the whole point of the question, and I almost missed it.”

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