LinkedIn SWE Interview: Coding Screen Guide

Estimated read time: 7-9 minutes

Summary: The LinkedIn SWE coding screen is the first major technical gate in many loops. The source supports a 45-60 minute phone or video screen with an engineer, usually in a shared editor, focused on algorithms, data structures, communication, edge cases, and complexity. This guide keeps the scope LinkedIn-specific and avoids importing Microsoft-wide assumptions that the source does not support.

TL;DR + FAQ (read this first)

At-a-glance takeaways

• The coding screen is reported as 45-60 minutes.
• You should expect an engineer and a shared-editor coding format.
• Supported topics include arrays, strings, hash maps, trees, graphs, dynamic programming, complexity, and edge cases.
• Seniority can change ambiguity and follow-up depth.
• Use LinkedIn-specific evidence, not parent-company assumptions.

Quick FAQ

Is the screen coding-heavy?
Yes. Coding has the strongest public evidence in the LinkedIn SWE source.

Does every candidate see it?
It is marked possible or role-dependent across intern through staff+ paths, and likely broad for SWE.

Should I prepare system design here?
Not for the screen unless told otherwise. Design is a separate, more senior or team-dependent signal.

What is the main gotcha?
Undercommunicating reasoning and missing edge cases.

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1) How the coding screen works

The source describes a phone or video screen with a shared editor and an engineer interviewer. You solve an algorithmic problem, explain your approach, handle edge cases, and discuss complexity.

Senior candidates may receive more ambiguous follow-ups, but the screen remains primarily a coding signal. Do not let broader design preparation replace the fundamentals.

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2) Coding questions you may face

The source gives topic themes rather than verified exact wording. These examples are written in realistic interview style from those themes.

• Given a string, return the longest substring that satisfies a character-frequency constraint. Then handle empty strings and repeated characters.
• Given a tree, return the nodes visible from the right side. Now explain how the solution changes for an iterative traversal.
• Given a graph of member connections, determine whether two members are connected within k hops.
• Given an array of profile update events, keep the latest value for each field and explain how you handle duplicate timestamps.
• Given a dynamic programming recurrence, compute the number of valid ways to reach a target state. Then reduce memory usage.
• Implement a data structure that supports insert, delete, and lookup by key. Then discuss expected time complexity.
• After coding, walk through edge cases for empty input, duplicates, missing nodes, and large input size.

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3) What strong performance looks like

Strong candidates clarify the input, choose a reasonable data structure, code cleanly, test edge cases, and explain complexity. They keep the interviewer oriented as they move from approach to implementation.

For senior candidates, follow-up handling matters. If the interviewer changes a constraint, restate the new goal and adjust rather than forcing the original solution.

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4) Common failure modes

Solving silently. The source highlights communication as part of the coding signal.

Skipping edge cases. LinkedIn coding reports repeatedly point to fundamentals and testing.

Overfitting to a memorized topic list. Prepare to adapt when the interviewer changes constraints.

Assuming Microsoft-wide process details apply. Keep the screen LinkedIn-specific.

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5) How to prepare

• Practice arrays, strings, hash maps, trees, graphs, and dynamic programming.
• Use a shared-editor style workflow with no reliance on autocomplete.
• Say the invariant and complexity before moving to follow-ups.
• Test empty input, duplicates, missing values, and large inputs.
• For senior levels, practice ambiguous follow-ups and tradeoff explanation.

The coding screen rewards clean fundamentals and clear communication under time pressure.

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