Rippling

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·Hiring Manager Screen

Jul 2026

Rippling software engineer interview that covered the usual background stuff plus a team selection exercise where you actually have to justify which team you'd join and why. More structured than I expected, felt like they wanted to see how you think about your own career, not just whether you can code.

Walk us through your current role and what you're actually responsible for day to day.
What are the main technologies and tools you work with regularly?
Tell me about a recent project you worked on, including what the goal was, what you contributed, what made it hard, and what the result was.
Out of the teams available, which one would you choose to interview with and why? Walk through how you're thinking about scope, tech stack fit, product impact, team culture, and your career goals.
What questions would you ask the hiring manager about this team?

“Pretty standard opener but I rambled a bit.”

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Rippling·Software Engineer·Technical Phone Screen

Jul 2026

Rippling software engineer round, one coding question the whole time. The problem looked straightforward but the edge cases around vote deduplication tripped me up more than I expected.

Design and implement a data structure to track users' upvotes and downvotes on articles. It needs a vote() method that handles duplicate votes correctly (same vote twice shouldn't create a new event, but flipping from up to down should), and a getLastVotes() method that returns the user's 3 most recent vote events in reverse chronological order.

“I went straight for two hashmaps, one to track current vote state per user/article pair and one to store the event log per user.”

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Rippling·Software Engineer·Hiring Manager Screen

Jul 2026

Hiring manager screen for a software engineer role at Rippling. Two questions, both pretty standard for this type of round, but Rippling specifically seems to care a lot about why you want to be there versus anywhere else.

Walk me through your past experience and the impact you've had.
Why do you want to join this company and this team specifically, not just any company hiring right now?

“I structured it as situation-action-result and tried to tie each role back to measurable outcomes.”

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Rippling·Software Engineer·Technical Phone Screen

Jul 2026

Rippling SWE interview that went pretty deep into algorithmic thinking and system design, all wrapped around one deceptively tricky problem. The follow-up about streaming updates was where things got interesting.

Given N driver online intervals (each with a start and end time) over a single day, find the maximum number of drivers who are simultaneously online at any point. Your solution should handle large N, overlapping intervals, and duplicate timestamps efficiently. Walk through your algorithm, implement it, and analyze its time and space complexity.
How would you extend your solution to support streaming updates, where intervals arrive incrementally and previously submitted intervals might be corrected or replaced?

“Classic sweep line problem but they wanted the full treatment.”

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Rippling·Software Engineer·Onsite - Coding / Algorithms

Jul 2026

Rippling software engineer coding round with two back-to-back implementation problems. The logger one was more open-ended than it looked, and the card game problem had a wildcard extension that I didn't fully finish.

Design and implement a configurable logger that supports message transformation behaviors like substring removal, truncation, uppercase conversion, and in-memory storage, with a search feature over stored messages.
Implement a five-card hand ranker for a simplified card game: parse hand and bid records, categorize each hand, sort from weakest to strongest, and compute a weighted score. Optionally add a wildcard mode where J acts as the best possible rank for category purposes but the lowest for tiebreaking.

“Looked deceptively simple at first.”

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Rippling·Software Engineer·Onsite - System Design / Architecture

Jul 2026

Rippling SWE interview, system design round focused on a single in-memory data structure problem. The problem looked deceptively simple but the O(1) constraint on getLast3Flips is where things get interesting. No behavioral stuff, just the one question the whole time.

Design an in-memory voting system for articles where users can vote up, down, or clear their vote. Support operations to cast/update a vote, get an article's score (ups minus downs), and retrieve the last 3 flip events for an article. A flip is specifically a direct up-to-down or down-to-up change, not a clear. All three operations must run in O(1).

“The score part is easy, just maintain a counter per article and adjust it on each vote call.”

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Rippling·Software Engineer·Technical Phone Screen

Jul 2026

Rippling SWE interview focused entirely on a payroll calculation problem that started simple and kept getting harder. Three parts total: basic shift pay, partial shifts with a cutoff time, then a caching/preprocessing follow-up. Felt more like a product-domain coding round than a pure algorithms session.

Given a list of workers each with an id, hourly rate, shift start time, and shift end time, write a function that computes the total payroll across all workers for their full shifts.
Extend the function to accept a cutoff time and compute total pay only up to that cutoff. Workers whose shifts end before the cutoff get full pay, workers who haven't started get nothing, and workers mid-shift get paid proportionally for the overlap.
If this payroll-up-to-cutoff function is called many times with different cutoff values, how would you redesign the approach to avoid recomputing from scratch on every call?

“Pretty straightforward warmup.”

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Rippling·Machine Learning Engineer·Technical Phone Screen

Jul 2026

Rippling ML engineer round that was basically a coding design exercise: build an expense rule engine, explain your data model out loud, then let AI generate the actual code. The follow-up on scaling and observability is where it got interesting.

Design and implement an expense rule engine that takes a stream of expense entries and a configurable rule set (category, amount thresholds, date ranges, employee level) to classify each expense as approved, flagged, or rejected, then return aggregated results per rule. AI coding tools are allowed, but you need to walk through your approach, data model, and rule evaluation logic before generating any code.
How would you scale this rule engine to handle high transaction volumes?
What monitoring metrics would you expose for this rule engine, specifically around rule hit rate, latency, and false-positive rate?

“The AI-allowed part sounds like a gift until you realize they want you to explain the design before touching the keyboard.”

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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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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 interview FAQ

What rounds are in the Rippling interview?

Candidates most often report a technical phone screen (60 write-ups), a system design round (48), a hiring manager screen (5) and an onsite coding round (5).

What does the Rippling technical phone screen cover?

Across 60 technical phone screen write-ups, the most tagged question topics are Algorithms & Data Structures (72 questions), Technical Trade-offs (71) and System Design (52).

What is asked in the Rippling system design round?

Across 48 system design write-ups, the most tagged question topics are System Design (130 questions), Technical Trade-offs (97) and Data Modeling (44).