Rippling AI Coding Interview: How the Round Works and How to Prepare

Rippling's AI coding round is a practical, multi-part coding problem that you may solve with an AI assistant while the interviewer watches how you use it. The problems are the ones Rippling also asks without AI: delivery cost tracking, an expense rule engine, a logger, card-hand ranking and a task scheduler. Reports describe interviewers looking at the design you settle before you prompt, how you check the code the assistant writes, and whether you can explain that code.

This page is built from reports in our bank. We hold 44 Rippling interview reports (counted on November 2, 2026), 42 of them for engineering roles. Of those 42, 22 describe a coding round where AI was allowed or required, and three more describe being offered the AI version and turning it down. Sixteen of the 22 describe a phone screen and seven an onsite round (two describe both), and one does not say which stage. For the rest of the loop, see our Rippling software engineer interview guide and the full list of Rippling interview questions.

Key takeaways

• Ask your recruiter whether AI is optional in your round. Ten reports describe it as optional or offered, one candidate was required to use HackerRank's built-in assistant, and another was told to use the assistant set up for the round.
• Settle the design before you prompt. Ten candidates talked through requirements and an approach with the interviewer first.
• Read every line the assistant writes. Reports describe AI code that broke money precision and AI code that never ran because of import errors.
• Pick your mode at the start. A candidate who began by hand and switched to AI with little time left called it the worst of both, and was rejected.

What is confirmed and what is only reported

Where the AI round sits in a Rippling loop

Most reports place it in the technical phone screen: one problem with two or three parts, where each part builds on the code you already wrote. Onsite, it sits next to a system design round and a project or hiring-manager conversation. Our Rippling system design guide covers the design round.

Two onsite reports describe something larger than a single problem:

• A senior candidate interviewing with Rippling's AI team had a 90-minute practical build: a chatbot that answers plain-language questions about employee expenses, with two JSON files standing in for the database and a streaming response. The candidate wrote that the interviewer could see the whole process of how they used AI (report).
• In another candidate's onsite, round two was an AI coding round on an existing HR backend: add a way to schedule an employee change, such as next month's promotion, so it takes effect on its date. The candidate's first idea, a new table plus a worker that applies changes as they come due, did not satisfy the interviewer (report).

Not every round allows AI. One report in our bank describes a one-hour CodePair interview where AI tools were not allowed. If your loop starts with an online assessment, that is a separate test, covered in our Rippling HackerRank OA guide.

Should you take the AI version?

Ten reports describe AI as optional or offered. Seven of those candidates used it and three wrote the code themselves. The reports disagree on what the choice changes: one candidate asked and was told the question is the same and only the evaluation differs (report), while another was told the AI version comes with a harder question (report).

How far you need to get is also unclear. One interviewer expected two of three parts. A candidate who ran out of time before part three believed the AI option meant finishing every part. One candidate chose to write the code by hand in both coding rounds, finished both follow-ups in the first, and got an offer (report).

Switching modes and under-using the assistant both went badly. One candidate started a long task-scheduler problem by hand, switched to AI with little time left and had it generate everything; they were rejected and call switching halfway the worst of both (report). A senior backend candidate was told to use the assistant "the way you normally would at work" without letting it make every decision. They barely touched it, think that cost them, and were cut after the round (report).

The problems reported in the AI round

Two reports cover two problems each, and one does not name its problem, so the column adds up to 23.

Delivery cost tracking: decide when to round before you prompt

Asked at Rippling: Bill Deliveries with Historical Rates and Deferred Rounding Drivers are paid by the hour, and a driver's rate can change from a given moment. Bill each delivery at the rate in force when it started, ignore a delivery ID you have already seen, and return the running total as dollars and cents.

The data structures are simple, a map of drivers and a running total, so the time goes on the follow-ups. Reports describe paying out all work up to a timestamp, reporting what is still unpaid, ignoring deliveries longer than three hours, and keeping old charges unchanged when a rate changes.

Three of the six delivery reports turn on money precision. The worked example in our practice version of the question shows why. At $15.15 an hour, a 1.5-hour delivery costs $22.725 and a 0.5-hour delivery costs $7.575. Rounding each delivery to the cent, with halves rounding up, gives a total of $30.31. Keeping the fractions and rounding once at the end gives $30.30, the answer that practice question expects.

The report behind that question says the interview's tests supported keeping the fractions and rounding once at the end, that the rule for an exact half cent was never confirmed, and that an AI debugging suggestion conflicted with that behavior (report). In another round, a copilot-style assistant changed the code so a value that should have been zero came out as a small fraction, and the candidate fixed the precision by hand (report). A third candidate kept every charge as an exact whole number, the rate in cents times the seconds worked, and converted to dollars only when returning the total (report).

Logger with handlers: design for the next handler

Asked at Rippling: Logger with Remove, Truncate, Uppercase and Store Handlers and Stored-Message Search A logger sends every message to a set of handlers. One removes a configured string, one truncates to N characters, one uppercases, and one stores the message without printing. A later follow-up searches the stored messages.

The code is short, and the questions candidates report are about design. One version of the prompt asks for a design that will take future handlers, such as one that writes to a database or one that calls a remote API, without changes to the core logger class (report). In another, the search follow-up matched a list of keywords and returned each message that contained any of them, with no duplicates, and the discussion moved on to punctuation, fuzzy matching and very large volumes (report).

One candidate agreed each handler's exact behavior with the interviewer, then had the assistant write the code and the unit tests (report). The candidate who got the database-and-API version finished the whole problem in Cursor and was rejected right after. They suspect the AI-written code was judged too good, though the report quotes no feedback from Rippling.

Card-hand ranking: custom rules an assistant will guess wrong

Asked at Rippling: Compare Two Five-Card Numeric Hands Cards are the digits 1 to 9. Rank two five-card hands by type, from five of a kind down to high card. When the types match, compare the cards from the last one dealt backwards, without sorting.

In this version two pair ranks above three of a kind, and ties are broken from right to left in dealing order. An assistant that fills in standard poker rules from memory gets both wrong, so copy the ranking from the problem into your prompt.

The follow-up gives you a partial hand. Two reports asked for its best and worst possible completion: a numeric five-card version and Camel Cards, where the interviewer accepted brute force and the hard part was the complexity analysis at the end. A third asked whether the winner was already decided, still unknown, or a tie. In that round GPT suggested comparing each player's best and worst possible hands, and the candidate could not explain the idea when the interviewer asked; time ran out before they finished (report).

Task scheduler: a filtered parent removes its whole subtree

Asked at Rippling: Propagate Task Filtering Through Ancestors Tasks form a forest: each has an ID, an optional parent and a flag saying whether an upstream rule filtered it out. Return the tasks that stay visible. A task disappears when it is filtered itself or when any of its ancestors is.

The report behind this question says an assistant given the problem as written does not cover this case. It gives a test with a filtered grandparent, its child and its grandchild, all three of which must disappear (report).

Other versions add more rules. The senior backend candidate mentioned earlier had a task list to deduplicate by description and due date, strip of completed tasks, sort by a given priority rule, with each subtask placed directly after its parent. Another round had three parts on filtering and sorting, and the interviewer expected two of them (report).

Expense rule engine: agree on how a rule is represented

Asked at Rippling: Build a Flexible Expense Rules Engine Managers set spending policies on company cards: for example no restaurant bill over $75, no airfare, and no trip over $2,000 in total. Design an engine that flags every expense and every trip that breaks a rule.

The rules come in two kinds, single-expense rules and trip-level totals, and the follow-ups build on that split: rules combined with AND and OR, new rules added through an API, a million expenses checked against ten thousand rules, and compound rules that share parts.

One candidate spent more than 20 minutes on requirements, edge cases and a class outline before asking the assistant to fill in the code, and passed the round (report). Another planned rules as operands and operators; the interviewer first agreed, then asked for a hardcoded evaluation function, so the code changed twice. The generated code also had import errors and never ran (report).

How candidates ran the round

Reports that describe their process follow roughly the same order:

1. Clarify. Ask about inputs, formats and edge cases. One candidate chose Unix timestamps in seconds after discussing time formats with the interviewer.
2. Design out loud. Name the classes and interfaces. Candidates described an abstract hand-type class, a Strategy pattern for ranking rules, and a logger that takes new handlers without changes.
3. Get agreement. One candidate let the assistant write code only after the interviewer agreed the approach was reasonable (report).
4. Prompt with your own spec. One candidate worked out the design and edge cases first and then gave the agent everything in a single prompt, which it implemented in one go. In another report the interviewer advised against pasting the whole problem into ChatGPT; the candidate found that rewriting it for the assistant took time and dropped constraints, which cost debugging time once the tests ran (report).
5. Review. Read the code before running it. Interviewers asked why the answer worked, and one asked what the candidate would have done differently writing it by hand.
6. Test. Walk through test cases with the interviewer, including the edge cases you listed in step 1.
7. Explain, then take the next part. One candidate also recommends saying so when your interview code would differ from production code.

Tools, screen sharing and what the interviewer sees

The reports name several setups. Three name HackerRank as the platform. In one of those the candidate had to use HackerRank's own assistant with its agent features on, and started in its plan mode; in another the problem was on HackerRank but the candidate used their own AI tool and IDE. Three name GPT or ChatGPT. One interviewer said any AI tool was fine, including the candidate's own IDE such as Cursor. Others mention a copilot-style assistant and an AI chat built into the coding pad.

Four reports describe sharing the screen while using AI, and three say it was required. One candidate joined from the browser version of Zoom, which kept dropping the window with the AI tool and muting them, and they blame their own setup for it (report).

If the round runs on HackerRank, treat everything you type to the assistant as part of your answer. HackerRank's AI-Assisted Interviews page says the interviewer can monitor your chat with the assistant in real time and the report includes the complete transcript. It also describes a guarded mode, which helps with syntax and concepts but does not write complete solutions, and an unguarded mode that works like everyday AI coding tools. Our HackerRank AI assistant guide covers guarded mode and the repository agent. No report says which mode Rippling uses, so ask your recruiter.

AI questions outside the coding round

Recruiters and hiring managers ask about AI as well. One recruiter told a candidate the coding interview would focus on using AI, optimization, speed and complexity (report). Another recruiter asked, during a 30-minute call, how the candidate uses AI day to day (report). A senior hiring-manager round asked whether code generated by a coding agent counts as code you wrote, and whether you had spread an AI tool or practice across your organization in the last six months (report).

Have one concrete example ready of how you use AI at work: what you hand to it, what you check yourself, and a time it was wrong. Two questions from our bank practice this: Explain How AI Supports Your Design and Coding Work and Staff Engineer Hiring-Manager Round: Platform Ownership, AI Adoption, and Cross-Team Influence.

Practice these on PracHub

Practice each one with an assistant switched on and a 60-minute timer, and explain the generated code out loud as you go.

Money and time

• Implement Courier Delivery Cost Tracking: the three core methods, then paying up to a timestamp.
• Delivery Cost Dashboard with Effective-Dated Driver Rates and Exact Money Totals: rate changes, duplicate IDs and exact totals.

Rules and extensibility

• Build a Flexible Expense Rules Engine: single-expense rules next to trip-level totals.
• Expense Policy Rules Engine: Scale, Violation Storage and Shared Compound Rules: the scale and storage follow-ups.
• Implement a Searchable Logger Pipeline: independent handlers, then keyword search and indexing.

Rules an assistant can get wrong

• Compare Complete and Partial Poker Hands: custom ranking, then partial hands.
• Find the Best and Worst Completion of a Partial Numeric Hand: the completion follow-up on its own.
• Task Scheduler with Dependencies, Priorities and Filters: many small conditions to list before you prompt.

Working in an existing codebase

• Add Scheduled Effective-Dated Employee Changes to an HR Backend: a feature in someone else's code.

Of our 136 Rippling questions, 13 carry the AI-assisted tag and are listed on Rippling AI-assisted questions. The tag misses several newer AI-round problems, including the task scheduler and some logger versions, so also browse Rippling coding questions.

A five-day plan

FAQ

Does Rippling allow AI in coding interviews?

Often, yes. Of the 42 Rippling engineering reports in our bank, 22 describe a coding round where AI was allowed or required, in the phone screen, the onsite or both, and three more candidates were offered it and declined. One report describes a CodePair interview where AI tools were not allowed, so ask your recruiter what applies to your loop.

Can I use my own AI tool, such as Cursor?

Sometimes. One interviewer told a candidate any AI tool was fine, including their own IDE such as Cursor, and two other candidates used their own setup. Another candidate was required to use HackerRank's built-in assistant. Confirm the tool before the day, and expect to share your screen.

Is the question harder if I choose AI?

Reports disagree. One candidate was told the questions are the same and only the evaluation differs; another was told the AI version comes with a harder question. In our reports the same problem families appear either way.

What problems come up in the Rippling AI coding round?

Delivery cost tracking (6 reports), a logger with handlers (4), card-hand ranking (4), a task scheduler (4) and an expense rule engine (3). Two onsite reports describe a larger build inside an existing app.

Can the interviewer see my prompts?

Assume so. Reports describe sharing your screen while you use the assistant, and HackerRank's help center says interviewers can watch the AI chat live and read the full transcript afterwards.

Should I paste the whole problem into the AI?

Reports advise against it. One interviewer said so directly, and another report says an assistant given the problem as written misses the case where a filtered parent must take its subtasks with it. Write your own spec from the requirements you agreed and prompt with that.

Where this comes from

Counts on this page come from the 44 published Rippling interview reports in our bank (42 for engineering roles) and its 136 Rippling questions, counted on November 2, 2026. HackerRank's AI-Assisted Interviews and AI Assistant in Interviews pages were read the same day. Two reports in the counts are not cited above: Coding, System Design, and AI Use, which says AI was allowed in a loop with three coding rounds, and An 11-Minute Late Start, a Chaotic Round, and a Recruiter Swap, from a candidate who chose the non-AI round.