Pinterest Software Engineer candidates report coding-heavy screens and loops, with recurring system design and behavioral evaluation. Prepare for a mix of algorithmic, practical engineering, and team-fit discussion.
$220K
Avg. Base Comp
$350K
Avg. Total Comp
5-6 rounds
Typical Rounds
2 months
Process Length
Pinterest Software Engineer interview reports vary by level and hiring route, but candidates commonly describe an early recruiter conversation followed by an assessment or live technical screen. Coding performance is the clearest shared preparation theme. Reported questions include graph traversal, strings and arrays, route reconstruction, sparse-matrix operations, and problems resembling topological sorting, trie-based autocomplete, and an LRU cache. Some interviewers expected candidates to identify an efficient direction quickly, so practice stating an initial approach, its complexity, and the optimization before writing code.
The broader technical mix can extend beyond algorithms. One accepted candidate encountered machine-learning fundamentals such as AUC, overfitting, vanishing gradients, and bagging versus boosting. Another senior candidate said system design was worth preparing for, although that report did not provide a detailed prompt. Treat these as level- or pipeline-dependent topics rather than universal stages.
Behavioral and manager conversations may cover motivation, prior work, and team fit. One IC14 candidate passed the technical interviews and then entered team matching, with an offer dependent on mutual interest with a manager or team. That candidate heard about the technical result roughly ten days after the onsite. Ask the recruiter whether you are interviewing for a named team or a broader hiring pipeline, and prepare to discuss the kind of team, charter, and manager environment you want if matching follows the technical evaluation.
Synthesized from 11 candidate reports by our editorial team.
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Real interview reports from people who went through the Pinterest process.
I cleared Pinterest’s IC14 onsite and moved into team matching rather than receiving an offer tied to a team immediately. I’m based in Canada. I had applied to a specific team on May 20, but a recruiter reached out over LinkedIn later that same day and the process ultimately was not framed around a particular role. I heard back about ten days after completing the onsite that I had passed the technical rounds.
The unusual part was what happened next: the recruiter explained that passing the technical interviews put me into conversations with specific engineering managers. An offer would come only once there was mutual interest between me and a manager/team, so the post-onsite phase was effectively about finding the right fit. I was looking for a team with a clear charter and reasonable work-life balance, since the technical hurdle was already behind me. I did not have a single team assigned throughout the process, although I know the experience can vary because many Pinterest openings are advertised as team-specific.
My main takeaway is to clarify the hiring model with the recruiter early, especially whether you are interviewing for a named team or for a general IC14 pipeline. If you pass the onsite, be ready to discuss the kind of charter, manager, and work-life balance you want during team matching, because that mutual-fit conversation determines whether an offer is issued.
Prep tip from this candidate
Ask your recruiter upfront whether the interview is tied to a specific team or uses post-onsite team matching. After passing, prepare concise criteria for the team charter, manager, and work-life balance you want, since mutual team fit determines the offer.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Pinterest
Write a query to return whether each user's subscription date range overlaps with any other completed subscription
| Question | |
|---|---|
| Size of Joins | |
| P-value to a Layman | |
| Most Repetition | |
| Priority Queue Using Linked List | |
| Ad Comments | |
| Feed Impression | |
| Max Width | |
| A/B Testing a Checkout Button Change | |
| Greater Release Dates | |
| Ranking Metrics | |
| Maximal Substring | |
| Singly Linked List | |
| Statistically Significant Test | |
| Maximum Common Substring | |
| 2nd Highest Salary | |
| Employee Salaries | |
| Merge Sorted Lists | |
| Empty Neighborhoods | |
| Top Three Salaries | |
| Monthly Customer Report | |
| Rolling Bank Transactions | |
| Top 3 Users | |
| Comments Histogram | |
| Raining in Seattle | |
| Job Recommendation | |
| Like Tracker | |
| Random SQL Sample | |
| Weighted Keys | |
| Largest Salary by Department |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report an initial recruiter or talent-acquisition conversation that may cover background, motivation for applying or changing companies, and possible team alignment. Some pipelines later use team matching, so clarify early whether the role is attached to a named team.
Candidates report an online assessment or a live coding screen before the main loop. Reported work includes dynamic programming, graphs, strings, arrays, and system-oriented coding; be ready to explain a workable approach, complexity, and an optimization when prompted.
One candidate reported three coding interviews within a five-round loop, while another reported programming interviews as part of a four-interview loop after a live screen. Topics reported across accounts include graph traversal, tries, hash maps, LRU cache, route reconstruction, sparse matrices, and practical React work.
Several candidates report a system-design or architecture interview. Examples mentioned include recommendation or feed-related architecture, large-scale data, and an elevator-scheduling problem. Typically, candidates should make assumptions explicit and explain tradeoffs rather than only naming components.
Candidates report behavioral or manager conversations covering project decisions, conflict, motivation, and team fit. For some successful IC14 candidates, Hiring Committee approval was followed by team matching, where discussions focused on backend strengths and preferred team characteristics.
