LinkedIn

LinkedIn uses the structured, platform-based format — CoderPad with their own selection of AI models, and the same multi-phase structure you see at Meta.

The platform and format

You log into a browser environment where the codebase, the AI chat panel, and the problem statement are already loaded. The interview walks the classic arc:

Because the structure is predictable, you can pace yourself: keep phase one fast, protect budget for the feature and optimization phases where the decisions live.

What to watch for

The AI panel on these platforms tends to be less capable than your daily tools — it lacks the system prompts and project context of a terminal agent. Candidates who rehearse with a weaker assistant handle this better.

Also remember that provided test cases are rarely comprehensive. They typically cover the happy path and maybe a couple of edge cases. Add your own for the corners the platform doesn't test — empty input, single elements, boundaries, duplicates — especially before you touch the optimization phase, when your test suite becomes the safety net for regressions.

Practice environment

LinkedIn, like Meta, offers a practice session on its platform before the real interview. Take it. The CoderPad AI interface has quirks — latency, refusal patterns, model quirks — that you won't meet in your own IDE. Thirty minutes of hands-on time is worth more than another hour of reading about it.

Tactical checklist

1. Orient in the first five minutes: entry point, data model, existing tests.
2. Plan the full arc before prompting.
3. Prompt with real class/method names from the codebase.
4. Run the suite after every generation; add edge-case tests early.
5. Before optimizing, run the full suite; after each change, run it again.