LinkedIn software engineer candidates report recruiter or OA entry points, live coding with follow-ups, project discussion, system-design fundamentals, and hiring-manager behavioral conversations.
$213K
Avg. Base Comp
$350K
Avg. Total Comp
3-5 rounds
Typical Rounds
3-5 weeks
Process Length
LinkedIn Software Engineer interviews reported here commonly begin with a recruiter conversation or technical phone screen, then move into live technical interviews and an onsite-style loop. Coding communication matters as much as reaching a solution. Candidates describe LeetCode-style medium and hard questions, with interviewers emphasizing test cases, time and space complexity, edge cases, optimization, and clear narration of the approach. Some candidates encountered multiple coding rounds, while recent staff-level reports also describe a separate AI-assisted coding format.
Project discussion recurs in phone screens and hiring-manager conversations. Be ready to explain the business context, architecture, technical challenges, operational issues, trade-offs, and impact of recent work. Clearly separate your contribution from the broader team’s work. Behavioral preparation should include examples of cross-team influence, ambiguous problems, leadership pushback, challenging projects, and engineering judgment.
System design is also reported, but its format varies. Candidates describe both standard design discussion and a more verbal conversation without visual aids. Practice clarifying requirements, structuring the response aloud, and defending design choices even when the interviewer offers limited feedback. For an AI-assisted coding round, explain how you direct the tool, review generated work critically, and independently validate correctness and complexity. The exact loop varies, so prepare for a mix of coding, design, project, and manager discussions rather than one fixed sequence.
Synthesized from 13 candidate reports by our editorial team.
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Real interview reports from people who went through the Linkedin process.
I got pretty lucky in the coding round because the main problem was maximum subarray sum, and I had practiced that pattern a few days before. The process started with a recruiter screen, then moved into a technical phone interview that was very focused on algorithms and data structures, and I also had to answer a few behavioral questions about my background and experience. After that there was a final onsite round. The whole thing felt pretty intense, but the structure itself was straightforward once I knew what each stage was testing.
The other technical screen I went through was live coding on HackerRank, but the interviewer explicitly cared more about explaining the logic than running the code. That round was based on Valid Palindrome, which was easy-level, and there was a follow-up question built off the same idea. The interviewer was friendly and tried to keep the atmosphere comfortable, which helped a lot. Overall, the process was more about clean communication and solid fundamentals than anything exotic. I ended up receiving an offer and accepted it, so I’d say the biggest takeaway is to be ready for classic algorithm questions, talk through your reasoning clearly, and not assume you need to brute-force the coding screen with execution.
Prep tip from this candidate
Be ready for classic array/string problems like maximum subarray sum and Valid Palindrome, and practice explaining your approach clearly without relying on running the code. Also expect at least one behavioral section alongside the technical screens.
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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 | |
|---|---|
| Over-Budget Projects | |
| Raining in Seattle | |
| Job Recommendation | |
| User Experience Percentage | |
| Bagging vs Boosting | |
| Month Over Month | |
| Integer to Roman | |
| Delivery Estimate Model | |
| The Brackets Problem | |
| Repeat Job Postings | |
| Nearest Common Ancestor | |
| String Mapping | |
| Reservoir Sampling Stream | |
| Find Duplicate Numbers in a List | |
| Target Indices | |
| Hurdles In Data Projects | |
| Biased Random Number Generator | |
| Real-Time Hashtag Partitioning | |
| Merge N Sorted Lists | |
| Target Value Search | |
| Same Characters | |
| Possible Triangles | |
| Type I and II Errors | |
| Max Width | |
| Binary Tree Validation | |
| Combinational Dice Rolls | |
| Optimistic vs Pessimistic Locking | |
| Scrapers or Users | |
| A Simpler KNN From Scratch |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report either an OA with two coding questions or an initial recruiter conversation focused on background, resume, past projects, and role fit. Some recruiter screens were brief; candidates may then receive a live technical screen.
Candidates report LeetCode-style coding on platforms such as HackerRank, including array, string, tree, binary-search, and cache problems. Interviewers may ask follow-ups, edge cases, test cases, and time/space complexity, so narrate the reasoning behind the implementation.
Candidates report multiple technical rounds in some loops, with medium and sometimes hard coding questions. A design discussion may cover backend or system-design fundamentals and can be conducted verbally; clarify constraints and explain trade-offs rather than assuming a whiteboard format.
Candidates report conversations about recent projects, individual responsibilities, design pros and cons, leadership, execution, and why LinkedIn. Prepare concise examples that distinguish your contribution from the team’s broader work.
One recent candidate reported a separate AI-assisted coding round. If this format appears in your loop, be prepared to direct the tool, explain what it generated, review its output critically, and independently validate correctness and complexity.
