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Rippling interview notes, page 12 of 13

Large Enterprises

Most of these Rippling write-ups come from Software Engineers (126), then Machine Learning Engineers (2). The most reported rounds are the technical phone screen (60), the system design round (48), the hiring manager screen (5) and the onsite coding round (5).

128 interview notes · updated Sep 2026

Rippling·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Rippling software engineer interview that was basically one big system design question: build a music player from scratch. More involved than I expected, they wanted class hierarchies, state machines, the whole thing.
  • Design and implement a music player system covering a song library, playlists, playback controls, playback modes (shuffle, repeat-one, repeat-all, off), and a now-playing queue distinct from playlists. Include class design, key method signatures, the play/pause/next/previous state machine under different mode combinations, and how you'd test edge cases like empty playlists or mid-song mode changes.

“This one ran long.”

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Rippling·Software Engineer·Technical Phone Screen

Jun 2026
Rippling SWE interview with a pretty dense algorithmic problem centered on delivery driver data. The focus was less on getting a working solution and more on whether you could reason through complexity tradeoffs out loud.
  • You have chronologically sorted delivery records for each driver. Given a timestamp t, how would you count the number of distinct drivers who were online at any point in the 24 hours before t? Walk through an optimized approach using per-driver binary search and explain the time complexity.

“The core idea clicked pretty fast: for each driver, binary search their sorted records to find entries that fall within the window, then count if at least one exists.”

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Rippling·Software Engineer·Technical Phone Screen

Jun 2026
Rippling software engineer interview, got hit with the median of two sorted arrays problem and they wanted the full O(log(m+n)) solution, not the lazy merge approach. Pretty classic hard leetcode but the explanation requirement made it trickier than just coding it up.
  • Given two sorted integer arrays, find the median of all their combined elements in O(log(m+n)) time. You need to explain the algorithm and prove its correctness, then implement it.

“This one stung a little because I knew the binary search partition approach conceptually but explaining WHY it's correct while also coding it is a different beast.”

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Rippling·Software Engineer·Onsite - Behavioral / Leadership

Jul 2026
Rippling software engineer interview with a project deep dive that goes way further than just describing what you built. They want to poke holes in your decisions.
  • What was the hardest part of this project, and why?

“This sounds like a warmup but it really isn't.”

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Rippling·Software Engineer·Technical Phone Screen

Jul 2026
Rippling's project deep-dive round for a software engineer role. You pick one of your own past projects and walk through it end-to-end with slides or a doc, then field technical follow-ups from there.
  • Walk us through a past project end-to-end: the problem, your role, the design decisions you made, the technical challenges you hit, the results, and what you'd change if you did it again.

“This is basically the whole round.”

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Rippling·Software Engineer·Onsite - System Design / Architecture

Jul 2026
System design round at Rippling for a software engineer role. The whole thing was centered on one big problem: design a personalized news aggregator, Google News style. Dense question with a lot of moving parts and some genuinely tricky follow-ups.
  • Design a personalized news aggregator similar to Google News. The system polls thousands of third-party publisher APIs, and users can follow publishers and select content categories. Personalization is limited to those two explicit signals only. Cover ingestion, storage, feed generation, and operations.
  • How would you handle polling thousands of publisher APIs without either wasting requests or missing new articles? What per-publisher state would you maintain?
  • Polling is inherently at-least-once delivery. How do you make sure seeing the same article twice doesn't cause problems?
  • A breaking story gets published by 50 different outlets within two minutes. Walk through how your deduplication pipeline clusters them and decides which single article a user sees.
  • A popular publisher's API starts returning 429 and 500 errors. How does your system protect freshness for all other publishers and recover this one automatically?
  • The product team wants to add engagement-based ranking on top of follows and categories. What parts of your design would need to change, and what stays the same?
  • Your p99 feed latency is fine but freshness p99 is 8 minutes against a 2-minute SLA. Where do you look first and what do you change?

“This one is bigger than it looks.”

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Rippling·Software Engineer·Technical Phone Screen

Jul 2026
Rippling SWE interview, one meaty coding problem that expands across three parts. The whole thing is basically one class you build up incrementally, which sounds manageable until part 3 shows up and you realize you've been underestimating it the whole time.
  • Design and implement a DeliverySystem class that tracks driver deliveries, total costs owed, and incremental payouts. Specifically: add drivers, record deliveries with a time interval and cost, return total cost per driver, and support partial payments that reduce an unpaid balance.
  • Extend the DeliverySystem to support a maxConcurrentDrivers query: given a time window, find the peak number of simultaneously active deliveries within that window, and return the count plus the exact sub-interval where that peak is continuously sustained.

“Parts 1 and 2 are pretty approachable.”

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Rippling·Software Engineer·Technical Phone Screen

Jul 2026
Rippling SWE interview with a poker hand comparison problem. The design angle was more interesting than I expected, less about grinding through cases and more about how you structure the solution.
  • Given a fixed set of poker hand-ranking rules, write a function that compares two players' hands and returns the winner. Input is two string arrays representing each player's cards.

“The problem itself isn't that hard but the design question buried inside it is.”

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Rippling·Software Engineer·Technical Phone Screen

Jul 2026
Rippling SWE interview that didn't go well for me. The coding problem wasn't impossible but I couldn't get it over the line, even with AI assistance.
  • Should you use a double or a long to represent timestamps? Justify your choice.
  • Given a driver object where each driver can have multiple driving records, implement a method payUpUntil(time) that returns the total salary across all records where endTime is less than the given time. Also write test cases for your implementation.

“Came up quick and felt like a warmup but I second-guessed myself.”

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Rippling·Software Engineer·Onsite - Multi Round

Jul 2026
Went through the full Rippling SWE loop: a coding round with a delivery cost tracking problem, a system design on hotel booking, and a hiring manager deep dive into a past project. Felt decent about all three but now sitting in post-final-round limbo four days out with no response after a polite follow-up.
  • Given a scenario involving delivery drivers, implement a solution to track and compute costs efficiently, including follow-up extensions.
  • Design a hotel booking system similar to a large travel platform, covering APIs, high-level architecture, data model, scaling, and how you'd prevent double bookings.
  • Walk through a significant past project in depth: the technical decisions you made, the tradeoffs involved, how it performed, and how you handled failures.

“Finished the main problem and all the follow-ups with time to spare, which felt good.”

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