← Back to Directory

Rippling interview notes, page 11 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·Technical Phone Screen

Jun 2026
Rippling SWE interview that went sideways in a weird way. The interviewer let me use AI tools, I finished fast, and then things kind of fell apart from there. Rejected the next day.
  • Design and implement a rate limiter.

“The interviewer said AI was fair game, so I used it and cranked out a solution pretty fast.”

View Post

Rippling·Software Engineer·Technical Phone Screen

Jun 2026
Rippling's AI coding round was a bit of a surprise format. You get to use an AI assistant during the problem, but the code still has to pass all the provided tests, so it's not a free pass by any means. Three sub-questions, all required, all around the same scenario.
  • Given a list of expense entries (each with fields like amount, category, date, employee, receipt, and approver) and a set of company policy rules (per-category caps, receipt requirements, approval thresholds, blackout categories, monthly totals per employee), implement a function that determines whether a submitted expense report is valid and returns any specific violations if not.

“The scenario sounds manageable until you realize the policy rules interact in non-obvious ways.”

View Post

Rippling·Software Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at Rippling for a software engineer role. The main question was a news aggregator, which sounds straightforward until you start thinking through the fan-out problems and personalization logic.
  • Design a news aggregator service similar to Google News, where users can subscribe to topics and publishers, see general top stories, and get personalized feeds with redirects to the original articles.

“I started with the basic read/write paths and got through the core subscription model okay.”

View Post

Rippling·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Rippling software engineer system design round focused on a news app, specifically read tracking and caching at scale. Pretty deep technically and they kept pushing on edge cases I hadn't thought through.
  • Design a simplified news app for a very large user base, covering both mobile and web. Users should see a personalized feed, be able to read articles, and track which articles they've already read.
  • How would you design the feed API to return a personalized ranked list of articles for a user within 200ms at P95, given 50 million daily active users and peak traffic around 1 million requests per second?
  • How would you store and sync a user's read state across devices, including handling brief offline periods without losing that data?
  • Walk through your caching strategy across the client, CDN, and server layers for both the feed and article content.
  • How would you handle personalization and cache invalidation together, especially for hot or trending content that many users are hitting simultaneously?

“This is basically the whole interview.”

View Post

Rippling·Software Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at Rippling for a software engineering role. One question, heavy focus on scale and throughput. Pretty standard for this type of interview but the emphasis on news aggregator specifics kept it interesting.
  • Design a news aggregator system similar to Google News, with a focus on high throughput and scalability.

“The scalability angle was where most of the conversation lived.”

View Post

Rippling·Software Engineer·Technical Phone Screen

Jun 2026
Got a tree/graph problem at Rippling for a software engineer role. The problem itself was pretty involved, two parts, and I spent more time than I'd like to admit just making sure I understood what 'level' meant before writing a single line of code.
  • Given an org chart represented as a tree with n employees, first find the maximum reporting depth (the longest root-to-leaf path measured in levels, where the CEO is level 1). Then, given a threshold h, find the minimum number of times you need to move a subtree directly under the CEO so that no employee exceeds level h.

“Two-part question and the parts are pretty different in difficulty.”

View Post

Rippling·Software Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at Rippling, focused entirely on metrics infrastructure. Pretty deep dive, they wanted both the client-facing SDK design and a full breakdown of how time-series databases work under the hood, including schemas.
  • Design the SDK API that client services would use to emit metrics to a centralized monitoring system.
  • How are time-series databases implemented? Walk through column layout, time partitioning, downsampling, and indexing, and explain why these make them fast for metrics workloads.
  • Provide table schemas for each component of the metrics monitoring system you're designing.

“I started with something like a `MetricsClient` class with methods for counters, gauges, and histograms.”

View Post

Rippling·Software Engineer·Onsite - Coding / Algorithms

Jun 2026
Interviewed for a Software Engineer role at Rippling and kicked myself the whole time because I'd seen the exact questions listed on a prep site beforehand but didn't pay for access. The coding rounds covered a Task scheduling problem, a Driver Delivery question, and a Rules Engine problem. There's also a mandatory AI-assisted coding round now where you need to have Cursor or something similar set up in your IDE.
  • Design and implement a task scheduling system.
  • Solve a coding problem involving driver delivery logistics and routing.
  • Build or reason through a rules engine system.
  • Complete a coding problem in an AI-assisted environment using a tool like Cursor integrated into your IDE.

“This is the one that stung the most.”

View Post

Rippling·Software Engineer·Technical Phone Screen

Jun 2026
Rippling SWE interview with a classic hard algorithm problem. Nothing too surprising about the topic but the complexity constraint is what makes it actually hard.
  • Given two sorted arrays of sizes m and n, find the median of the combined elements in O(log(m+n)) time.

“The naive approach is obvious, merge and find the middle, but they want logarithmic time so you have to do binary search on the partition.”

View Post

Rippling·Software Engineer·Technical Phone Screen

Jun 2026
Rippling software engineering interview with a data aggregation problem that looked deceptively simple at first glance. The constraint about a million records is what made it interesting, since a naive approach would get wrecked on repeated full scans.
  • Given a list of expense records (each with an employee ID, trip ID, category, and amount), implement functions to compute: total spend per employee across all records, total spend per trip for a given employee, and total spend per category for a given employee. The solution needs to handle up to a million records efficiently without re-scanning the full list on every query.

“My first instinct was to just filter and sum on each call, which works fine for small inputs but obviously falls apart at scale.”

View Post