Rippling’s AI coding interview: what candidates report

Published 4 November 2026 · 3 min read

Rippling lets candidates use AI in its coding rounds, both in the phone screen and onsite. Rippling hasn’t published a description of the round, so this guide is built from candidate reports between March and August 2026, including one from a candidate who got an offer.

The format

• Any AI tool, HackerRank for the code. An offer holder in San Francisco said both the phone screen and the onsite coding rounds allowed ChatGPT, Cursor, Claude or whatever they normally use. The code still had to run in HackerRank, so they copied it across from their own IDE (LeetCode Discuss).
• A few house rules. A senior candidate could paste the prompt into Cursor as long as the code went back into HackerRank and local files and chat history were deleted afterwards (LeetCode Discuss).
• About 60 minutes, live. One candidate described a 60-minute live round on a business-style problem (LeetCode Discuss).
• Sometimes a choice. A senior candidate in August was offered either an AI-assisted round or a traditional one for the onsite coding round, and chose to code without AI (r/OfferEngineering). A candidate on Blind was also told they could choose whether to use AI (Blind).

What the problems look like

Not LeetCode puzzles. Candidates describe practical, object-oriented problems with simple algorithms and a lot of code to write:

• An expense policy engine: take card transactions, apply configurable rules, and return approved or rejected with a reason.
• A three-part problem that the senior candidate doubts they could have finished without AI.
• The offer holder said the algorithms were easy and the work was closer to object-oriented design.

How one candidate spent the hour

The expense-engine candidate split the 60 minutes like this:

1. First 10 minutes: design only. Name the entities and clarify how rules combine.
2. Next 25 minutes: the AI scaffolds the data models and boilerplate, while they write the core rule-evaluation logic themselves.
3. Last 20 minutes: edge cases, with AI-generated tests they walked through one by one.

What seems to be graded

Candidates aren’t sure; the offer holder said they didn’t know how AI use was graded until the recruiter called. The pattern in the reports:

• You lead, the AI types. The senior candidate talked through their approach before the AI did anything and wrote explicit prompts, so it was clear the AI only sped up the typing.
• Finishing matters. There are several parts and a lot of simple code, which is why candidates lean on the AI.
• Tests and edge cases close the round in the reports that describe the end.

How to prepare

1. Set up your editor and AI before the call, and practise moving code into HackerRank quickly.
2. Pick a fast model. The senior candidate’s tip: there’s a lot of simple code, so a top model is overkill.
3. Practise business-rule problems: rule engines, approval flows, small domain models with clear entities.
4. Rehearse the design-first habit on a timer. Our free practice projects have a feature phase built around this.

Read every Rippling report we have, with sources, on the Rippling reports page.

Sources

• LeetCode Discuss post, March 2026: offer holder, any AI tool, code runs in HackerRank
• LeetCode Discuss post, June 2026: senior AI coding round
• LeetCode Discuss post, June 2026: expense policy engine, 60 minutes
• r/OfferEngineering thread, August 2026: choice of AI or traditional round
• Blind thread: senior phone screen, told they could choose whether to use AI