Katkat

Company

Pinterest interview questions

161 practice questions in the style of Pinterest's interviews, covering Software Engineering Fundamentals, Data Manipulation (SQL/Python), Analytics & Experimentation, Statistics & Math, Coding & Algorithms, Machine Learning, Behavioral & Leadership, System Design, ML System Design.

Questions161

8 easy107 medium46 hard

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Pinterest interview questions by the numbers

Last updated

Katkat has 161 Pinterest interview questions. The largest topics are Coding & Algorithms (42%), System Design (12%) and Analytics & Experimentation (11%). By difficulty: 5% easy, 66% medium and 29% hard. The most common round is Onsite (56%). Frequent concepts include System Design, Data Structures, Algorithms and Distributed Systems.

Difficulty

  • Easy5% · 8
  • Medium66% · 107
  • Hard29% · 46

Interview rounds

  • Onsite56% · 90
  • Technical Screen39% · 63
  • Online Assessment5% · 8

Roles

  • Software Engineer43% · 70
  • Data Scientist35% · 56
  • Machine Learning Engineer19% · 30
  • Data Analyst1% · 2
  • Data Engineer1% · 2

Frequent concepts

  • System Design6% · 10
  • Data Structures6% · 9
  • Algorithms4% · 6
  • Distributed Systems4% · 6
  • Scalability4% · 6
  • A/B Testing3% · 5

Showing 91–120 of 161 results

  • This problem tests graph traversal algorithms and efficient membership checking within a directed acyclic graph (DAG) representing hierarchical organizational permissions.

    PinterestMachine Learning EngineerCoding & AlgorithmsOnsite
  • This question evaluates proficiency in randomized algorithms and probabilistic sampling, with emphasis on numerical precision, large-weight handling, and memory-versus-accuracy trade-offs. Commonly asked in Coding & Algorithms interviews for data-science roles, it assesses practical application ability grounded in conceptual understanding of numerical stability and relevant data-structure choices.

    PinterestData ScientistCoding & AlgorithmsOnsite
  • This question tests your ability to design an A/B test for a feed-based UI feature, focusing on metric selection, counter-metrics, and the identification of potential cannibalization effects.

    PinterestData ScientistAnalytics & ExperimentationTechnical Screen
  • This question tests your ability to design a comprehensive evaluation framework for a product feature launch, specifically focusing on metric selection, causal impact measurement, and addressing potential cannibalization effects.

    PinterestData ScientistAnalytics & ExperimentationTechnical Screen
  • This question evaluates understanding of algorithmic search and data manipulation skills, specifically applying binary search over date-ordered logs and composing efficient queries using SQL and Python.

    PinterestData ScientistData Manipulation (SQL/Python)Onsite
  • Tests practical statistical computing skills by requiring the implementation of bootstrap confidence intervals and jackknife variance estimation for arbitrary metrics evaluated on pandas DataFrames.

    PinterestData ScientistStatistics & MathOnsite
  • This question evaluates competency in streaming algorithms, dynamic data structures for maintaining top-K elements in real time, and scalability considerations for handling large or distributed input streams.

    PinterestData ScientistCoding & AlgorithmsOnsite
  • This question evaluates algorithmic problem-solving and basic number-theory competency by requiring generation of prime numbers with attention to correctness and performance characteristics.

    PinterestData ScientistCoding & AlgorithmsOnsite
  • Design and implement a regional access control system that handles time-based access overrides on a static tree hierarchy with out-of-order updates.

    PinterestSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
  • Implement weighted random selection from scratch and compute delete-1 jackknife estimates of parameter bias and standard error for an arbitrary statistical estimator.

    PinterestData ScientistCoding & AlgorithmsTechnical Screen
  • This question evaluates a candidate's ability to implement a constant-time LRU cache and reason about thread-safety and concurrent access, testing data structure design and concurrency competencies in the Coding & Algorithms domain.

    PinterestData ScientistCoding & AlgorithmsOnsite
  • This question evaluates understanding of probabilistic classification, specifically competence in estimating class priors, feature likelihood parameters, and computing posterior scores for Naive Bayes variants.

    PinterestMachine Learning EngineerMachine LearningOnline Assessment
  • Design and implement a memory-efficient sparse matrix class supporting entry retrieval, updates, addition, and matrix multiplication.

    PinterestMachine Learning EngineerCoding & AlgorithmsTechnical Screen
  • Design an interactive autocomplete system that tracks query frequencies, returns the top 3 ranking prefix matches per keystroke, and commits completed queries to dynamic history.

    PinterestMachine Learning EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates a candidate's competency in data structures (priority queues/min-heaps), online algorithm design for stream processing, and complexity and memory analysis.

    PinterestSoftware EngineerCoding & AlgorithmsTechnical Screen
  • Practice a DS-focused Python similarity question using Pin representations, cosine similarity, top-k ranking, and explicit edge-case handling. Extend the implementation discussion to vectorized scaling, approximate retrieval, offline ranking metrics, leakage checks, diversity, and online evaluation.

    PinterestData ScientistData Manipulation (SQL/Python)Technical Screen
  • This question evaluates competency in designing and implementing efficient dynamic interval data structures, handling correctness for duplicate and nested intervals, extending APIs with cancellation and point queries, and performing rigorous worst-case time/space analysis and resource-adaptation.

    PinterestData ScientistCoding & AlgorithmsTechnical Screen
  • This question evaluates a candidate's competency in ensemble learning and practical implementation skills, including bootstrap sampling, decision tree base learners, aggregation via majority voting with tie-breaking, and managing data using pure Python lists and standard library routines.

    PinterestMachine Learning EngineerMachine LearningOnline Assessment
  • This problem tests your ability to implement weighted random sampling from an array, focusing on probability distribution handling and efficient selection algorithms.

    PinterestData ScientistCoding & AlgorithmsTechnical Screen
  • This problem tests your ability to design a memory-efficient representation for sparse matrices and implement arithmetic operations while leveraging the sparsity of the data.

    PinterestSoftware EngineerCoding & AlgorithmsOnsite
  • This question evaluates streaming I/O and buffered string-processing competency for Part 1 and transaction netting with balance computation for Part 2, covering skills in memory-bounded parsing, data-structure bookkeeping, and algorithmic flow reasoning.

    PinterestSoftware EngineerCoding & AlgorithmsOnsite
  • Implement multiplyStrings(a, b) to multiply two non-negative integers given as decimal strings, returning the product as a string without any big-integer or arbitrary-precision library. The solution covers grade-school O(n*m) long multiplication with linear memory, the Karatsuba and FFT/NTT scaling alternatives, correct carry and leading/trailing-zero handling, and a full edge-case test plan.

    PinterestSoftware EngineerCoding & AlgorithmsOnsite
  • This question evaluates proficiency in string-based numeric processing, arbitrary-precision arithmetic concepts, precise rounding rules including deterministic tie-breaking, and handling of sign and decimal edge cases, and falls under the Coding & Algorithms domain.

    PinterestMachine Learning EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates proficiency in mobile UI architecture, grid data structures, connected-component algorithm reasoning, and state management concerns such as undo and memory/stack safety within the mobile app development and iOS domain.

    PinterestSoftware EngineerSoftware Engineering FundamentalsOnsite
  • This problem tests your ability to implement a space-efficient Trie data structure capable of storing strings along with associated weights to return top-k suggestions for a given prefix.

    PinterestMachine Learning EngineerCoding & AlgorithmsOnsite
  • This problem tests your ability to implement efficient weighted random selection, specifically focusing on cumulative distribution functions and binary search to achieve optimal query performance.

    PinterestData ScientistCoding & AlgorithmsTechnical Screen
  • Tests backtracking with operator precedence handling and operand formatting to construct all valid arithmetic expressions evaluating to a target value.

    PinterestSoftware EngineerCoding & AlgorithmsOnsite
  • This question evaluates statistical inference and experimentation skills, including A/B test hypothesis formulation with a predefined MDE, estimation of absolute and relative lift with confidence intervals, proportion testing for CTR and retention, sample ratio mismatch detection, and multiple-comparison adjustments.

    PinterestData ScientistAnalytics & ExperimentationTechnical Screen
  • Evaluates quasi-experimental analysis and product-metric judgment after a homepage launch lacks a randomized control. Strong answers compare matching and propensity weighting, audit experiment hygiene, choose metrics, and diagnose mixed CTR results.

    PinterestData ScientistAnalytics & ExperimentationOnsite
  • Design a high-scale merchant data enrichment platform that ingests, reconciles, and maintains up-to-date business attributes (addresses, social metrics, wiki metadata) across heterogeneous third-party sources with strong provenance and fault tolerance.

    PinterestSoftware EngineerSystem DesignOnsite

Pinterest interview experiences

All 24 →
  • Splitwise Question in the Tech Screen, Ran Out of Time on the Follow-upPinterest · Data Engineer · Technical Screen · Oct 2026
    Rejected

    Their DE role is titled SDE too, and the interview process is the same as the SDE one. I prepared a dozen or so questions from interview reports, but I didn't prepare this one. I stumbled my way through writing it, and the interviewer gave me a lot of hints. I blame myself for not preparing enough. I just moved on. The Splitwise question, the one listed for Pinterest. It is exactly the same probl…

  • Full Loop, Rejected on the Newly Added AI Coding RoundPinterest · Software Engineer · Onsite · Sep 2026
    Rejected

    Phone screen: LeetCode 1055 It was split into two parts. Part 1 only checks whether the target can be formed at all; once that ran and passed, I did the second part. For Part 2 I first tried recursion + greedy, but it had a bug and couldn't guarantee the optimal solution. After fixing that I wrote a recursive version that passed. But the interviewer wanted an iterative approach, so the rest was t…

  • Onsite With Two System Design Rounds and Escalating Follow-upsPinterest · Software Engineer · Onsite · Sep 2026

    Round 1 — Behavioral A standard set of leadership questions, one by one: Self-introduction Pick a project where you had the strongest ownership, or learned the most from, and go deep on it How did you measure success for this project? What's the strongest team environment you've been part of? A time you had to make a fast decision and live with the consequences A time you got constructive feedbac…

  • Pin-Board BFS Phone Screen, All Tests Passed, Still RejectedPinterest · Software Engineer · Technical Screen · Sep 2026
    Rejected

    This is a question other people have already posted. The input is three parameters. The first one is the pins that belong to each topic board. For example, a travel board has pins for maple leaves, ocean waves, and a lake, and a home board has pins for a bedroom, a living room, and renovation. So one topic covers an array of pins. The second parameter is the starting pin, and the third parameter…

  • Three Parts Passed Before Time Ran OutPinterest · Software Engineer · Technical Screen · Sep 2026

    The phone screen used the familiar Pins Violation Log problem. The input was a list of violation logs already sorted by timestamp. Each record had the form [postId, policy, timestamp]. There were five parts: Count unique posts by policy. Get the policies violated by a post. Count unique posts at a timestamp. Count unique posts in a time range. Count violations by policy in a time range. I had to…

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