Katkat

Company

Rippling interview questions

106 practice questions in the style of Rippling's interviews, covering Coding & Algorithms, Behavioral & Leadership, System Design, Software Engineering Fundamentals, Data Manipulation (SQL/Python), Machine Learning.

Questions106

7 easy75 medium24 hard

Your progress0/106

Rippling interview questions by the numbers

Last updated

Katkat has 106 Rippling interview questions. The largest topics are System Design (32%), Software Engineering Fundamentals (29%) and Coding & Algorithms (27%). By difficulty: 7% easy, 71% medium and 23% hard. The most common round is Technical Screen (60%). Frequent concepts include System Design, Distributed Systems, Caching and Concurrency.

Difficulty

  • Easy7% · 7
  • Medium71% · 75
  • Hard23% · 24

Interview rounds

  • Technical Screen60% · 64
  • Onsite33% · 35
  • HR Screen6% · 6
  • Online Assessment1% · 1

Roles

  • Software Engineer95% · 101
  • Backend Engineer3% · 3
  • Data Analyst1% · 1
  • Machine Learning Engineer1% · 1

Frequent concepts

  • System Design17% · 18
  • Distributed Systems9% · 10
  • Caching8% · 9
  • Concurrency8% · 8
  • Scalability8% · 8
  • API Design7% · 7

Showing 1–30 of 106 results

  • This question evaluates a candidate's ability to perform efficient data aggregation, use associative data structures (maps/dictionaries), and reason about time and space complexity when processing large collections of records.

    RipplingSoftware EngineerCoding & AlgorithmsOnsite
  • This question evaluates a candidate's ability to handle interpersonal conflict within a high-stakes technical environment and their capacity to align personal motivations with the company's business model.

    RipplingSoftware EngineerBehavioral & LeadershipOnsite
  • Bill deliveries with start-time historical rates, duplicate suppression, overlapping jobs, exact sub-cent accumulation, and configurable final half-up or half-even rounding.

    RipplingSoftware EngineerCoding & AlgorithmsTechnical Screen
  • A multi-part system-design interview covering tagged event aggregation, driver-pay accounting, employee offboarding, and an LLM-backed expense assistant. Candidates must distinguish approximate dashboards from exact billing and design reliable workflows across failing dependencies.

    RipplingSoftware EngineerSystem DesignOnsite
  • Design a billing dashboard for a bike-share fleet that prices each ride at the per-minute rate in effect when it started, supports rate changes that take effect at a future timestamp without repricing past rides, and reports an exact running total as a formatted string.

    RipplingSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
  • This question tests your ability to design a rule evaluation engine that processes structured expense data against a set of logical conditions, including support for basic operators.

    RipplingSoftware EngineerCoding & AlgorithmsTechnical Screen
  • Reason about graph common ancestors by clarifying edge direction, DAG versus tree structure, self-ancestry, output semantics, and reachability algorithms.

    RipplingSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
  • Analyze currency conversion queries and the reported maximum-amount backtracking follow-up without inventing missing rate or path rules. The solution shows how direction, precision, path consistency, same-currency behavior, and profitable cycles determine the graph algorithm and whether a finite answer exists.

    RipplingSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
  • Implement a poker hand evaluator that determines whether an incomplete poker hand can be completed from the remaining deck to beat an opponent's complete five-card hand.

    RipplingSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates implementation of card-hand evaluation logic, combinatorial reasoning about incomplete information, and deterministic tie-breaking rules for comparing complete and partial poker-like hands.

    RipplingSoftware EngineerCoding & AlgorithmsOnsite
  • Compare two five-digit numeric hands under a nonstandard multiplicity ranking and original-position right-to-left tie-break, using extensible category evaluators rather than standard poker rules.

    RipplingSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates proficiency in algorithms and temporal data handling, including the use of efficient search techniques and complexity reasoning to count distinct active drivers over a sliding 24-hour window.

    RipplingSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates proficiency in reasoning about time-interval overlap, event aggregation, and online data structures for streaming interval queries, testing skills in algorithm design and temporal data handling within the Coding & Algorithms domain.

    RipplingSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This English summary evaluates the candidate's proficiency in algorithm design and complexity analysis within the Coding & Algorithms domain, specifically for aggregating interval data and computing concurrent counts.

    RipplingSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates algorithmic problem-solving with sorted arrays, focusing on concepts like binary search, partitioning strategies, complexity analysis, and careful handling of edge cases when computing order statistics.

    RipplingSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates skills in interval-overlap reasoning, sweep-line algorithm design, event creation and deduplication, comparator and tie-breaking subtleties, and asymptotic time/space complexity analysis within the Coding & Algorithms domain.

    RipplingSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates understanding of graph modeling and path-optimization in directed weighted graphs, specifically reasoning about chained conversions and maximizing multiplicative path weights.

    RipplingSoftware EngineerCoding & AlgorithmsTechnical Screen
  • Design a stateful tracker class to record courier delivery intervals and earnings, query running courier payouts, and calculate peak concurrent active deliveries within a specific time window.

    RipplingSoftware EngineerCoding & AlgorithmsTechnical Screen
  • Design and implement an in-memory payroll tracker for hourly-paid delivery drivers that records delivery intervals, reports total and unpaid labor cost, and settles wages up to a given time without ever paying twice. It tests stateful class design, idempotent settlement, treatment of in-progress and late-recorded work, and exact money arithmetic.

    RipplingSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
  • This problem tests your ability to design an extensible rule-evaluation engine using object-oriented principles, specifically focusing on decoupling evaluation logic from business requirements.

    RipplingSoftware EngineerSoftware Engineering FundamentalsOnsite
  • Design a scalable topic-based news aggregation system covering distributed feed scheduling, idempotent article ingestion, stable pagination, and a low-latency read path.

    RipplingSoftware EngineerSystem DesignTechnical Screen
  • Design raw and computed spreadsheet views with formula parsing, dependency replacement, cache invalidation, atomic edits, and undo semantics.

    RipplingSoftware EngineerSoftware Engineering FundamentalsOnsite
  • Model five-card Camel Cards hands with an abstract hand type class and a hand class, compare two hands by type and tie-break, then find the best and worst complete hands consistent with a partial hand. Tests object-oriented design, classification logic and step-by-step complexity analysis.

    RipplingSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
  • Design an extensible, data-driven expense policy engine that evaluates structured JSON rules against submitted expenses without requiring code deployments.

    RipplingSoftware EngineerSoftware Engineering FundamentalsOnsite
  • Design a news aggregation and recommendation system that ingests multi-publisher articles, clusters coverage of identical real-world events, and serves a personalized, deduplicated feed with low latency.

    RipplingSoftware EngineerSystem DesignTechnical Screen
  • Design an in-memory driver payroll system that records time-stamped earnings and supports efficient range queries for both individual drivers and company-wide aggregates.

    RipplingSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates system design skills for building a large-scale, multi-region news aggregation platform, assessing competencies in ingestion architecture, deduplication and clustering, indexing and retrieval, personalization and ranking, and operational concerns like monitoring, consistency, and compliance.

    RipplingSoftware EngineerSystem DesignOnsite
  • This question tests your ability to design a distributed system that manages transactional integrity, high concurrency during bookings, and efficient searching for room availability.

    RipplingSoftware EngineerSystem DesignOnsite
  • Design an end-to-end hotel search and booking platform balancing high-throughput search with strictly consistent, transactional room inventory management across continuous date ranges.

    RipplingSoftware EngineerSystem DesignOnsite
  • Design an extensible, thread-safe in-process logging framework that supports multiple isolated handlers, immutable log records, backpressure handling, and dynamic handler registration.

    RipplingSoftware EngineerSoftware Engineering FundamentalsTechnical Screen

Rippling interview experiences

All 33 →
  • Delivery Cost Calculation with an AI AssistantRippling · Software Engineer · Technical Screen · Oct 2026
    In progress

    I did the Rippling technical screen last week. It was online coding with an AI assistant. The problem was the Delivery Cost calculation that has shown up here before (the calculate_compensation version). Summary of the problem: a food delivery platform pays drivers per delivery, and the cost is duration × that driver's hourly rate. You implement a dashboard system whose core is a single number, t…

  • Phone Screen Where You Choose Whether to Use AIRippling · Software Engineer · Senior+ · Technical Screen · Oct 2026

    This was the first technical interview after HR screen. The interviewer asked me do I want to use AI or not, and said if I picked AI I would get a harder question. I chose not to use AI and this was the question I needed to solve in 45 minutes: Design a delivery-driver payment system add_driver(driver_id, usd_hourly_rate) — register a driver with an hourly rate. record_delivery(driver_id, start_t…

  • Pluggable Logger Phone Screen, Rejected After Using CursorRippling · Software Engineer · Technical Screen · Oct 2026
    Rejected

    Ran into a new question. / Your task is to build a simple logging tool for an application. The core requirement is that the Logger should support several different log handlers, and it should be easy to add new log handlers in the future without modifying the core Logger class. Implement these first: Filter handler: before printing, remove all occurrences of a (configurable) string from the log m…

  • An Onsite Where the Coding Round Expected You to Use AIRippling · Backend Engineer · Senior+ · Onsite · Sep 2026
    Rejected

    Interviewed for a Senior backend role in July. Round 1 — Coding (60 min), AI use explicitly allowed The interviewer (Data Cloud team) opened by saying directly: "The environment has an AI assistant set up, use it the way you normally would at work, just don't let it make every decision for you." Problem: task scheduler. You're given a batch of tasks (description / due date / priority / completed…

  • Article Vote TrackerRippling · Software Engineer · Technical Screen · Sep 2026

    Got the Article Vote Tracker question. Part 1 involved adding articles, recording up/down votes, detecting vote flips, and returning the last 3 unique flipped articles. The follow-up asked how to handle a very large flip history. The key optimization is to maintain only the last 3 unique flipped article IDs per user instead of storing the complete history. I completed Part 1 but did not proceed t…

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