Rippling Interview Questions — Page 9

Rippling·Software Engineer·Technical Phone Screen

Jun 2026

Rippling SWE interview had a follow-up coding question that was more nuanced than it first appeared.

Implement a get_top_k(k) function where each upvote adds +1 and each downvote subtracts 1 from an article's score, and you return the top k articles by score.

“Seemed straightforward until I started thinking about whether to sort every time or maintain a heap.”

Rippling·Software Engineer·Technical Phone Screen

Jun 2026

Rippling software engineer screening with two back-to-back coding problems. Nothing too exotic but the follow-ups on the first one had some teeth to them.

Design an in-memory balance tracker for drivers on a delivery platform. It needs to support applying a delta to a driver's balance, fetching one driver's balance, and fetching the total balance across all drivers. Follow-up: how do you keep get_total_balance efficient when it's called very frequently? Second follow-up: how would you answer queries about the cumulative total after the first k updates in a sequence?
Given a list of closed intervals, merge all overlapping ones and return the result sorted by start time.

“The naive version took me like two minutes, just a hashmap and a running total variable.”

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Rippling·Software Engineer·Onsite - System Design / Architecture

Jun 2026

System design round at Rippling for a software engineer role. The question was a deep one about building a real-time delivery dashboard and it covered basically every dimension of distributed systems you can think of.

Design a real-time dashboard for a delivery platform that displays live driver locations, order statuses, ETAs, and aggregate metrics like active driver counts and orders in progress. Cover data sources, ingestion, update frequency, storage, APIs, caching, deduplication, out-of-order event handling, consistency vs availability trade-offs, scaling, fault tolerance, and latency optimization.

“This question is basically a whole system design course crammed into 45 minutes.”

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Rippling·Software Engineer·Onsite - System Design / Architecture

Jun 2026

Rippling system design round focused entirely on building a centralized logging platform from scratch. Pretty brutal scope, the kind of question where you can talk for an hour and still feel like you barely scratched the surface.

Design a centralized logging system for a large organization, covering ingest from thousands of hosts, near-real-time search, configurable retention, high write throughput with burst handling, reliability, and multi-tenant isolation.
A single tenant suddenly produces 100x their normal log volume during an incident. Walk through what happens at each stage of your pipeline and whether other tenants are affected.
A team needs to search logs from 60 days ago using free-text on the message body, but your hot window is only 7 days. How does that query execute and what would it cost to make 60-day free-text search feasible?
Your search cluster goes down for 3 hours during peak traffic. What data is lost, what is delayed, and how does recovery work? What sizing decision determines whether you lose data at all?
Compare an inverted-index search engine like Elasticsearch against a columnar store like ClickHouse for the hot tier of a logging system. When would you pick each?
How would you prevent a mapping explosion caused by a tenant logging thousands of distinct high-cardinality JSON keys, without simply rejecting their logs?

“This is the kind of question where you spend five minutes just figuring out where to start.”

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Rippling·Software Engineer·Onsite - System Design / Architecture

Jun 2026

Rippling SWE interview that was basically a full OOP design session. They gave me a poker hand comparison problem and expected a real extensible design, not just brute-force if-else. Took me a while to see where they were going with the pluggable type registry angle.

Design an object-oriented system to compare two poker hands, determine a winner or a tie, and keep the design extensible for new hand types and ranking rules.
How would you implement a generic tie-breaking rule for same-type hands (like two three-of-a-kinds) without writing bespoke logic for every hand type?
Implement an `add_type` function that registers a new hand type with a detection function, and an `evaluate` function that accepts an ordered list of type names representing the ruleset's precedence.
If multiple registered type predicates match the same hand (e.g. a full house also satisfies a 'contains a triple' predicate), how does your design guarantee a deterministic classification?
How would your design change to support Texas Hold'em, where each player picks the best 5-card hand out of 7 available cards?
How would you add suit-based tie-breaking to rulesets that need it, without affecting rulesets that don't?
How would you handle wild cards or jokers that can substitute for any rank?

“My first instinct was to write a big switch statement mapping hand type to an integer rank and call it a day.”

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Rippling·Software Engineer·Onsite - System Design / Architecture

Jun 2026

System design round at Rippling for a software engineer role, focused entirely on building an ad performance measurement platform from scratch. Pretty intense scope, covering ingestion, stream processing, enrichment, and serving layers all in one session.

Design a data platform to measure advertising performance, handling impression and click events from mobile and web clients at up to 500k events per second.
What are the trade-offs between sending one request per event versus batching multiple events in a single request, particularly for mobile clients?
How would you handle deduplication and out-of-order or late-arriving events in the pipeline?
How would you enrich streaming events with campaign and ad metadata, and what are the trade-offs between stream-stream joins, stream-table joins, and batch enrichment?
What data model and storage would you use to serve both near-real-time dashboard queries and historical batch reports over months of data?

“This is the main question and it's a lot.”

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Rippling·Software Engineer·Onsite - System Design / Architecture

Jun 2026

System design round at Rippling for a software engineer role. The whole thing was basically one big question about news aggregation at scale, and it went pretty deep into crawling, indexing, and ML serving. Walked out feeling okay about it but not great.

Design a large-scale news aggregation system similar to Google News, covering web crawling, search and recommendation indexing, and feed serving with a P99 latency target around 100ms.
How would you handle deduplication of similar news stories coming from multiple different sources?
What are the tradeoffs between crawl freshness and index update latency in a news system, and how would you balance them?
How would you optimize ML model serving to meet strict feed latency requirements in a pull-based architecture?

“This one sprawled in every direction.”

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Rippling·Software Engineer·Technical Phone Screen

Jun 2026

Rippling SWE interview with a classic interval merging problem. Nothing too wild but it's the kind of question where you can really embarrass yourself if you fumble the edge cases.

Given an array of intervals, merge all overlapping ones and return the resulting non-overlapping intervals.

“Sort by start time first, that part came to me quickly.”

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Rippling·Software Engineer·Technical Phone Screen

Jun 2026

Rippling SWE interview that built on a previous poker hand problem, this time adding incomplete data into the mix. The twist made it way more interesting than I expected, and the 'better approach' they were fishing for took me a minute to fully articulate.

You're extending a poker hand comparison function to handle incomplete hands where some cards may be missing due to network issues. Given two partial hands, implement a function that returns 'win', 'loss', 'tie', or 'unknown'. The optimal approach: compute best-case and worst-case hand strength for each player, and only return a definitive result if one player's worst case strictly beats the other's best case.

“My first instinct was the naive version: only return a result when both hands are complete, otherwise just say 'unknown'.”

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Rippling·Software Engineer·Onsite - System Design / Architecture

Jun 2026

Rippling SWE interview with a meaty system design problem around building a rule engine for expense approvals. The question had a lot of moving parts and the discussion kept branching into follow-ups I wasn't fully ready for.

Design a rule engine for expense approval: given a set of rules with predicates and actions, evaluate incoming expense objects and return a decision.
How would you handle conflicts when multiple rules match the same expense?
How would you extend the predicate system to support compound conditions with AND, OR, and NOT logic?
How would you allow new rules to be added dynamically without redeploying the service?

“I started with the data model which felt like the safe entry point.”

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