Candidates describe a LinkedIn Software Engineer loop of recruiter contact, an online assessment or technical screen, then coding, design and hiring-manager rounds. LeetCode-style mediums and project discussion dominate.
$190K
Avg. Base Comp
$256K
Avg. Total Comp
4-7 rounds
Typical Rounds
Not reported
Process Length
Candidates describe LinkedIn's Software Engineer interview as a mostly LeetCode-style process where follow-ups and clear explanation matter as much as the first solution. Reports mention recruiter screens, an online assessment with two coding questions, and a virtual onsite that included a technical round and a hiring-manager conversation. Others describe longer loops with several coding rounds, system design and a manager round.
Problems candidates cite include maximum subarray sum, grouping anagrams, a tree problem, weighted random pick with a binary-search follow-up, a string manipulation problem extended with a concurrency follow-up, and an LRU cache. For the LRU cache, practice the constant-time hash map plus doubly linked list design and be ready to improve a working answer. One candidate was also asked how to scale a cache with 100x more reads than updates. Several reports stress running test cases aloud and explaining time and space complexity as you go.
Project discussion comes up repeatedly, especially with the hiring manager, along with why LinkedIn and trade-offs in past decisions. System design experiences vary: one candidate had a verbal-only round with a shifting prompt, and CAP theorem came up once with a request for a real-world example. One report mentions debugging a large code sample with AI involved, so be ready to narrate your reasoning aloud.
Synthesized from 32 candidate reports by our editorial team.
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Real interview reports from people who went through the Linkedin process.
The system design round was the only part of my LinkedIn onsite where I felt I needed help. I was interviewing at the Staff Software Engineer level in the US, and the loop included coding, an AI coding round, craftsmanship, a host/leader conversation, and system design, following a phone screen. The phone screen came first, and I advanced to the onsite after passing it.
The regular coding round was a variation on a LinkedIn-tagged LeetCode-style problem rather than an exact repeat. The AI coding round was a newer addition and was less intimidating than I expected: I described at a high level how I would solve the problem and use AI as part of the workflow. The important part was not blindly accepting an answer. I was asked why I would accept the AI-generated solution, so I had to defend it and demonstrate that I was still in control of the reasoning.
I felt strong about phone screen, coding, and AI coding. Craftsmanship and the host/leader round were harder for me to self-evaluate; the host manager conversation focused on past experience and did not feel like a standard behavioral round. In system design, I needed one hint before I got to the core data structure, largely because I blanked on its name. My overall impression is that LinkedIn is very selective, especially for system design, so I would not over-read your own perception of how each round went. The outcome was not shared. For prep, practice variations of LinkedIn-tagged coding and system-design questions, and rehearse explaining why you would trust, reject, or revise an AI-proposed solution rather than simply using it.
Prep tip from this candidate
Prepare to explain and defend AI-assisted solutions: articulate your own approach first, then justify why an AI suggestion is correct or how you would revise it. Also expect variations of LinkedIn-tagged coding and system-design questions rather than exact repeats.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Linkedin
Given two sorted lists, write a function to merge them into one sorted list.
| Question | |
|---|---|
| User Experience Percentage | |
| Over-Budget Projects | |
| Raining in Seattle | |
| Job Recommendation | |
| Green Dot | |
| Month Over Month | |
| Bagging vs Boosting | |
| Integer to Roman | |
| Delivery Estimate Model | |
| The Brackets Problem | |
| Type I and II Errors | |
| Repeat Job Postings | |
| Nearest Common Ancestor | |
| Groups of Anagrams | |
| String Mapping | |
| Ranking Metrics | |
| Find Duplicate Numbers in a List | |
| 180 Day Job Postings | |
| Reservoir Sampling Stream | |
| Target Indices | |
| Biased Random Number Generator | |
| Hurdles In Data Projects | |
| Real-Time Hashtag Partitioning | |
| Newsfeed Model | |
| Evaluate News | |
| Merge N Sorted Lists | |
| Multi-Reaction | |
| Target Value Search | |
| Same Characters |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report an initial recruiter call or screen that is often short and focused on your resume, past experience and motivation. Some describe basic behavioral questions. Expect to explain why LinkedIn and summarize recent projects concisely, since those themes typically return in later rounds.
Some candidates report an online assessment with two or three coding questions, while others describe a live technical phone screen. Reported screen problems include a string manipulation problem with a concurrency follow-up and a weighted random pick problem with a binary-search follow-up. Expect follow-ups that change the shape of the problem.
Candidates describe onsite or virtual coding rounds at LeetCode medium level, sometimes with two questions per round, including harder ones. Reported topics include arrays, trees, anagram grouping, binary search follow-ups and LRU cache. Interviewers may probe complexity, test cases and optimization, so talk through your reasoning aloud.
Not every candidate describes the same design experience. One reports a verbal-only design round with no visual aids, another a CAP theorem discussion with a real-world example, and one was asked how to scale an LRU cache with 100x more reads than updates. Prepare to reason about production trade-offs clearly, whatever the format.
Candidates typically describe a conversation with the hiring manager about past projects, the pros and cons of design decisions, and standard behavioral questions such as why LinkedIn and tell me about yourself. Prepare one or two projects in depth, including trade-offs and what you would change.
