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Rippling interview notes, page 8 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 system design round for a software engineer role, focused on building a rule engine from scratch. The problem was open-ended enough that you had to make real design decisions before writing a single line of code, which I wasn't fully prepared for.
  • Design and implement a rule engine where evaluate_rules(rules, expenses) returns the expense or trip records that violate any rule. You pick the rule schema and return type. Justify your return-type choice before writing code, and design for extensibility given that new rule types and an API for rule creation are coming.

“The justify-before-coding part tripped me up more than the actual implementation.”

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Rippling·Software Engineer·Technical Phone Screen

Jun 2026
Rippling software engineer interview that centered on a lengthy project deep dive, roughly 30 to 40 minutes, covering everything from architecture decisions to mentorship. Pretty thorough for a single round.
  • Walk me through a project you're proud of: what was the problem, what was your specific role, what technical approach did you take, what trade-offs did you make, and what did success actually look like?
  • What was the hardest problem you ran into on that project? How did you figure out what was wrong, what options did you consider, and what was the measurable result of how you fixed it?
  • Tell me about a time you mentored someone, whether a teammate or an intern. How did you structure the relationship, how did you track their growth, and what would you change if you did it again?

“This is the kind of question where you think you're prepared and then 20 minutes in you realize you've been rambling about architecture and forgot to mention what the actual goal was.”

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Rippling·Software Engineer·Technical Phone Screen

Jun 2026
Rippling software engineering interview with a poker hand comparison problem that had two parts: clean implementation first, then a tricky follow-up about partial hands with missing cards. The problem looked straightforward until the second part showed up.
  • Build a hand comparison engine for a simplified poker-like game. Given two 5-card hands, determine which player wins based on standard hand rankings (four of a kind, full house, three of a kind, two pair, one pair, high card) with full tie-breaking logic.
  • Extend the solution to handle partial hands where one or both players have fewer than 5 cards. Determine if the outcome is already forced across all possible completions from the remaining deck, or return 'unknown' if the result could still go either way.

“The ranking itself wasn't bad to implement but the tie-breaking rules are where I slowed down.”

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Rippling·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Rippling system design round focused on extending an expense rule engine to handle trip-level aggregations. The problem was meaty and had a lot of moving parts, more than I expected for a single session.
  • You have an existing expense rule evaluation system. Extend it to support trip-level aggregate rules, such as a cap on total trip expenses and a cap on total meal expenses per trip. Trip '002' violates both rules in the sample data. How do you redesign the system and update the return type of the rule evaluation function to surface both per-expense and per-trip violations?

“This is the kind of question where you feel okay for the first five minutes and then realize you've been thinking too small.”

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Rippling·Software Engineer·Technical Phone Screen

Jun 2026
Rippling SWE interview with a coding problem that had a tricky follow-up about handling incomplete inputs. Nothing too wild but the edge case caught me off guard.
  • Given two poker hands, determine which one wins. Follow-up: if both hands are incomplete, output UNKNOWN when a winner cannot be determined (e.g. 9999 vs 9).

“The base case was manageable but the follow-up tripped me up.”

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Rippling·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Rippling system design round, one big open-ended question about building an expense rules engine from scratch. The scope was wider than I expected and I spent probably too long on the condition modeling before they nudged me toward the engine logic itself.
  • Design and implement an expense rules engine that takes an expense record and a configurable set of rules, then returns a decision of APPROVE, FLAG_FOR_REVIEW, or REJECT. Cover class design, rule combination policies, extensibility for new condition types, and how you'd test the system.

“I started by talking through the data model for an expense record which felt safe, but the real meat was the condition abstraction.”

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Rippling·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Rippling system design round focused entirely on building an ads-click aggregation pipeline from scratch. Pretty deep technically, they wanted real trade-off reasoning not just a block diagram.
  • Design an ads-click aggregation pipeline that ingests click events from mobile and web clients, enriches them by joining with user, campaign, and item data, and serves downstream consumers with near-real-time results.
  • Walk through the trade-offs between batching click events on the client side versus sending them individually. How does that decision affect latency, data loss risk, and server load?
  • What schema and transport format would you choose for the click events, and why?
  • How would you design the near-real-time aggregation layer, and what guarantees can you realistically offer downstream consumers?

“This is a big question and I underestimated how many sub-topics they'd want to cover.”

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Rippling·Software Engineer·Technical Phone Screen

Jun 2026
Rippling software engineer interview, one round focused entirely on building an in-memory delivery cost and payment tracking system. The problem was more accounting-flavored than I expected for a coding screen, which threw me off a bit at first.
  • Design and implement an in-memory system for tracking delivery costs and driver payments, with APIs for adding drivers with hourly rates, recording deliveries with start and end times, getting the total cost, paying up to a given timestamp, and querying unpaid costs.

“The hourly rate angle made this feel less like a typical coding question and more like a mini payroll engine.”

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Rippling·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Rippling system design round for a software engineering role. The whole thing was one big open-ended question about scaling a rule engine, no coding, just talking through architecture for about an hour. Pretty intense if you haven't thought about predicate indexing before.
  • You have an expense-approval rule engine where rules are predicates over fields like amount, category, and employee ID, each paired with an action like approve, reject, or escalate. How would you scale this to handle 1 million rules evaluated against roughly 1,000 expenses per second? Cover indexing strategies, sharding, caching, hot-loading rules, and latency targets.

“This question is massive and I did not fully appreciate that until about 15 minutes in when I realized I'd only talked about one piece.”

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Rippling·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Rippling SWE interview that was basically a full system design mini-project: design and build a backend for an article voting system as a small web API. One question but it sprawled into data modeling, concurrency, scaling, and API design all at once.
  • Design and implement the backend for an article voting system exposed as a web API. Articles have an id, author, title, body, and creation timestamp. Users can upvote, downvote, or retract their vote (one vote per user per article, changeable). Provide endpoints for creating articles, fetching by id, voting operations, getting a score, and getting the top-k articles by score. Cover your data model, endpoint design, time complexity per operation, concurrency handling, vote manipulation prevention, and how you'd scale.

“This one sprawled fast.”

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