Candidates report Rippling software engineer screens built around practical coding, such as extensible logger and rules-engine designs, followed by system design and manager conversations. Most reports here ended in rejection.
$201K
Avg. Base Comp
$300K
Avg. Total Comp
3-5 rounds
Typical Rounds
2-3 weeks
Process Length
Rippling software engineer candidates describe screens that lean toward practical, product-style coding rather than pure puzzles. Several recent reports mention extensible design problems: a logger that grows to support multiple logger types, search and production-debugging follow-ups, and a corporate credit-card rules engine covering ban rules, per-expense limits and trip totals. In these reports the problem keeps growing, so speed on the base solution decides whether you reach the follow-ups. One candidate says they ran short of time on the follow-up of a 35-minute logger round.
AI tools appear in some recent rounds. One candidate says an opt-in format used Cursor; another says AI was allowed in a phone screen but that overly verbose prompts cost time. A third was asked to write a class-and-function spec before prompting an LLM, then lost the round to an exact decimal-rounding mismatch. Plan a brief design first, keep prompts short, and check output formatting early.
System design shows up in both screens and onsite loops. One candidate got an open-ended screen question and was told breadth mattered. Another got a news aggregator and was later told the data model (keys, tables, SQL versus NoSQL) needed more depth. Another report mentions a payment-system prompt. Older reports mention a recruiter call, a hiring manager call, LRU cache, 3 Sum and graph problems, and SQL questions for an intern role. Experiences vary by level and year, and nearly all reports here ended without an offer. Expect some variation in format.
Synthesized from 18 candidate reports by our editorial team.
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Featured question at Rippling
Write a function to determine whether or not two rectangles overlap.
| Question | |
|---|---|
| Month Over Month | |
| Subscription Retention | |
| Three Indexes Adding Zero | |
| Shortest Path Algorithms | |
| Swipe Payment API | |
| Scalable Data Pipelines | |
| LRU Cache 1 | |
| 2nd Highest Salary | |
| Top Three Salaries | |
| Merge Sorted Lists | |
| Closest SAT Scores | |
| Subscription Overlap | |
| Empty Neighborhoods | |
| Monthly Customer Report | |
| Rolling Bank Transactions | |
| Prime to N | |
| Top 3 Users | |
| Random SQL Sample | |
| String Shift | |
| Comments Histogram | |
| Largest Salary by Department | |
| Upsell Transactions | |
| Raining in Seattle | |
| Minimum Change | |
| Customer Orders | |
| Find the Missing Number | |
| Bagging vs Boosting | |
| Find the First Non-Repeating Character in a String | |
| Size of Joins |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report an initial recruiter conversation covering background, projects and why Rippling. Some describe it as brief and light on technical depth, while one reports a short technical question. It may also decide the outcome early, so a concise walkthrough of recent work is worth preparing.
Several candidates report a timed screen, sometimes around 35 minutes, with a base problem plus follow-ups. Reported prompts include an extensible logger with search, a corporate credit-card rules engine, and building a Quora-like API. Tests and clean abstractions are reportedly expected, and AI use may be optional.
Some candidates report more traditional problems: an LRU cache built from scratch with complexity discussion, 3 Sum, string manipulation, and a weighted graph path problem. Reports differ on style, and one candidate found the interviewer's direction on the graph problem confusing. Expect to explain tradeoffs while coding.
Candidates report system design in both screens and onsite loops, including a news aggregator, a payment system and an open-ended popular prompt. Feedback suggests going beyond scaling to concrete schema, keys and SQL versus NoSQL tradeoffs, and stating assumptions explicitly so they can be confirmed.
Candidates describe hiring manager or engineering manager discussions that are mostly behavioral, covering background, projects and situational questions such as balancing backend automation against new features. One candidate reports a meeting with the EM of the team after positive recruiter feedback and then a delay before hearing back.
