Pinterest interview notes, page 17 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

Jun 2026

Pinterest SWE interview with a group expense settlement problem. Pretty classic balance-sheet style coding question but the edge cases kept me on my toes longer than I'd like to admit.

Given a list of group trip transactions where each transaction has a payer, a total amount, and a list of people splitting the cost equally, compute a minimal (or any valid) set of direct transfers that brings everyone's net balance to zero.

“I got the basic structure pretty fast: compute each person's net balance by adding what they paid and subtracting their share of each transaction.”

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Pinterest·Data Scientist·Technical Phone Screen

Jun 2026

Pinterest data scientist interview with a heavy stats focus, basically a two-week A/B test scenario where they wanted you to walk through the whole significance testing pipeline live. Pretty rigorous for a technical screen.

Given two weeks of experiment data comparing active minutes between a control and treatment group, which statistical test would you choose and why? Walk through the assumptions and how you'd verify them.
Calculate the p-value and 95% confidence interval for the experiment results and explain what each one actually tells you.
How would you think about Type I and Type II errors in this experiment context, and if the team was also tracking five secondary metrics, how would you handle the multiple comparisons problem?

“I went straight to the two-sample t-test and talked through normality and equal variance assumptions.”

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Pinterest·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Pinterest ML engineer interview with a fill-in-the-blank coding format, which I wasn't expecting at all. They gave you a template and you had to implement Naive Bayes from scratch, computing priors, posteriors, the whole thing. NumPy was fair game at least.

Given a code template with blanks, implement Naive Bayes classification including computing the prior and posterior probabilities.

“The fill-in-the-blank format threw me off more than the actual math did.”

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Pinterest·Data Scientist·Technical Phone Screen

Jun 2026

Pinterest data scientist interview focused heavily on experimentation design and product metrics for the homepage feed. The questions were legitimately hard and assumed you already knew causal inference basics, so if you're going in cold on matching vs weighting you'll feel it.

An intern accidentally launched an experiment with no control group. How would you still estimate the treatment effect?
Compare matching and propensity score weighting as approaches for estimating causal effects. What are the real trade-offs between them?
Walk through an existing A/B test setup and identify common pitfalls that could bias the results.
You're launching a new horizontal feed module on the homepage. What primary metrics would you use to measure whether it's successful?
After launching a treatment, you see homepage click-through rate drop while DAU and time-spent remain flat. How do you investigate and which user segments do you look at first?

“This one tripped me up because my first instinct was to say 'you can't' and then backtrack.”

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

Jun 2026

Pinterest ML Engineer system design round, basically one long question about building a fraud detection system from scratch. Dense interview, they wanted end-to-end coverage and pushed back on every shortcut I tried to take.

Design a real-time fraud detection system for banking transactions end to end, covering problem framing, features, model selection, serving infrastructure, feedback loops, and key trade-offs.
What features would you engineer for a fraud detection model, and how do you handle velocity and graph-based features specifically?
How would you serve model predictions under 100ms, and how do you maintain consistency between your offline training features and online serving features?
How do you think about the precision vs recall trade-off in fraud detection, and how does that change your model threshold decisions?
How do you handle adversarial adaptation, meaning fraudsters changing their behavior specifically to evade your model over time?

“This was basically the whole interview compressed into one question.”

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Pinterest·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Pinterest MLE interview focused on a system design coding problem around access control with group hierarchies. The problem escalated from a simple dictionary approach into a proper tree traversal, which is where things got interesting.

Design and implement an access-control class for advertisers and a group hierarchy (e.g., world > country > city), where children inherit access from parent groups. The API needs grant_access, revoke_access, and check_access methods. Start with a flat dictionary, then optimize to a tree-backed implementation where grant and revoke are O(1) and check_access walks up the tree in O(depth).

“My first instinct was to just store a set of (advertiser, group) pairs and call it a day.”

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Pinterest·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Pinterest MLE interview with a coding question around prefix search on a sorted word list. Pretty algorithmic for an ML role, but not shocking given how Pinterest structures their loops.

Given a lexicographically sorted list of words and a list of prefix queries, return for each query the index of the first word that starts with that prefix, or -1 if no such word exists.

“My first instinct was trie, which felt clean for batched queries.”

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Pinterest·Data Scientist·Technical Phone Screen

Jun 2026

Quick Python screen for a Data Scientist role at Pinterest. One coding question, felt more like a warmup than a real technical deep-dive, but they clearly wanted to see if you knew your way around algorithmic thinking in Python.

Write a Python function that returns the first n prime numbers.

“Went with trial division first just to show I understood the basics, then mentioned a sieve as the cleaner option.”

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Pinterest·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Pinterest ML engineer screen, rapid-fire theory questions back to back. Nothing crazy but you need to have your definitions locked in because there's no time to think out loud.

What is overfitting in machine learning?
What are some ways to prevent overfitting?
What is the difference between L1 and L2 regularization?

“Straightforward enough.”

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Pinterest·Product Manager·Onsite - Product Sense / Strategy

Jun 2026

Pinterest PM interview with a product design question that felt oddly Meta-flavored. One question, not a lot of context given upfront, and I had to figure out the scope on the fly.

How would you design a birth date capture flow for a social platform?

“My first instinct was to just say 'put a date picker in onboarding' and move on, which would have been embarrassing.”

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