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Pinterest interview notes, page 21 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·Hiring Manager Screen

Jun 2026
Interviewed at Pinterest, question was about hiring philosophy and how you'd actually screen for what matters. Pretty open-ended, felt more like a leadership conversation than a traditional interview question.
  • If you were building out your team, what qualities would you prioritize in candidates and how would you actually test for those things during interviews?

“I rambled a bit here.”

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Pinterest·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Interviewed for an ML engineer role at Pinterest, got asked about data drift. Pretty straightforward round, just one concept question but it required more depth than I expected.
  • What is data drift and how does it affect machine learning models in production?

“I knew the term but fumbled explaining it clearly under pressure.”

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Pinterest·Data Scientist·Technical Phone Screen

Jun 2026
Pinterest data scientist interview with a pretty involved SQL question. One round, heavy on query writing and optimization thinking. Felt like a legitimate problem they actually care about, not a leetcode warmup.
  • Given two tables tracking lists and their items, write a SQL query that finds the unordered pair of recently created lists with the highest item overlap. Return the pair's IDs (smaller first), the overlap count, and the Jaccard similarity. Handle deduplication, ties, and be ready to explain indexing for scale plus a top-5 variant.

“This one took me a minute to even parse.”

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Pinterest·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Pinterest system design round focused entirely on rate limiting, and it went deeper than I expected. They really wanted you to think through the full stack from algorithm choice to distributed coordination, not just sketch a box diagram.
  • Design a rate limiter for a high-traffic API that enforces per-user, per-API-key, and per-IP limits, handles excess requests gracefully, and returns proper 429 responses with Retry-After headers.
  • Walk through the trade-offs between fixed window, sliding window log, sliding window counter, token bucket, and leaky bucket algorithms for rate limiting.
  • How would you handle distributed coordination for a rate limiter deployed across many API gateway instances, specifically using Redis?
  • How do you handle edge cases like clock skew between gateway nodes, hot keys, and enforcing multiple simultaneous rate limit tiers?
  • What are the implications of choosing fail-open versus fail-closed semantics for a rate limiter when the coordination layer becomes unavailable?

“I started with token bucket because it felt natural for burst handling, but the interviewer kept pushing on accuracy under load and I realized I hadn't thought carefully about what happens when you have all three limit types active at once.”

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Pinterest·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Pinterest ML engineer interview, just one question worth noting but it was a pretty fundamental one about regularization that I fumbled more than I expected to.
  • With L1 and L2 regularization, why does L1 tend to push many model parameters all the way to zero while L2 typically doesn't?

“I knew the answer lived somewhere in the geometry of the penalty terms but I started rambling about sparsity before actually explaining the 'why'.”

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Pinterest·Software Engineer·Technical Phone Screen

Jun 2026
Pinterest SWE interview with a masonry layout simulation problem. Pretty focused on getting the greedy assignment logic right and then talking through complexity. Nothing too wild but the problem had some subtle tie-breaking rules that could trip you up.
  • Given a list of pin heights and a fixed number of columns, simulate a masonry-style layout by assigning each pin to the column with the smallest current total height (breaking ties by column index). Return the final column assignments or total heights, and analyze time and space complexity.

“The greedy part clicked pretty fast for me.”

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Pinterest·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Pinterest product analytics question, one of those metric discrepancy cases where you have two numbers moving in opposite directions and you have to explain why. Pretty classic but still stressful in the moment.
  • Pinterest's weekly active users are up 5% but email notification open rates dropped 2%. What's going on?

“I jumped straight to 'more users means more emails sent, so the denominator grew faster than the opens' and the interviewer just kind of waited.”

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Pinterest·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Interviewed for an ML engineer role at Pinterest, got a theory question about optimization fundamentals. Pretty standard technical screen vibe, nothing too wild.
  • What are the differences between convex and nonconvex functions?

“I knew the basics but fumbled a bit explaining why it actually matters for training.”

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Pinterest·Software Engineer·Hiring Manager Screen

Jul 2026
Interviewed for a bizops role at Pinterest, just one question to go off of here but it was a classic data storytelling prompt that I probably underprepared for.
  • Walk me through a project where you had to turn raw data into actionable insights for a team or client.

“I picked a project I knew well but rambled about the data cleaning part way too long.”

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Pinterest·Data Scientist·Technical Phone Screen

Jul 2026
Pinterest Data Scientist interview with a system design flavored coding question around streaming data. The problem was well-scoped but the follow-up on distribution is where things got interesting.
  • Design a data structure that continuously ingests a stream of integers and can return the top-K largest values seen so far at any point. How would you scale this for very large K or across distributed data streams?

“Started with a min-heap of size K which felt right, and they seemed fine with that.”

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