LinkedIn's AI-Enabled Coding Interview: How to Prepare

LinkedIn has an AI-enabled coding round as part of their onsite loop. It replaces one of the traditional coding rounds, so instead of two standard coding interviews, you'll have one traditional and one with an AI assistant available.

The format is more grounded than most AI-enabled interviews out there. The problems are well-known patterns (caches, interval problems, data processing), the code volume is manageable, and the AI is there as a helper rather than the centerpiece. Where things get interesting is in the follow-ups. LinkedIn pushes past the initial solution into concurrency, multithreading, synchronization, and how you'd productionize what you just built.

I've talked to candidates at both senior and Staff levels who've gone through this round. The consistent takeaway is that the coding problem itself isn't where people struggle. It's managing time between solving, using AI appropriately, and showing depth when the interviewer pivots to production concerns.

If you're looking for a broader overview of AI-enabled coding interviews across all companies, check out our complete AI-coding interview guide.

LinkedIn's onsite loop includes one AI-enabled coding round alongside a traditional coding round. The AI round replaces what used to be a second standard coding interview.

The rest of the loop varies by level and role, but for engineering positions you can expect system design, a craftsmanship round (think code quality and engineering practices), and a hiring manager chat alongside the two coding rounds.

The interview runs on CoderPad. The UI has a code editor in the center and an AI assistant panel on the right. Some candidates have reported a file explorer on the left, while others described a simpler layout with just the editor and AI panel. This likely depends on the specific problem you get.

You get a choice of AI models, typically Claude and Opus. Go with whichever is the most capable option available. The model quality can make a real difference in how useful the responses are, so don't just pick the default without checking.

The AI assistant sits in the chat panel on the right side, but it can't directly modify your code. You type a prompt, it gives you a response, and then you manually copy the relevant parts into the editor yourself. It's a small thing, but if you're used to Copilot-style inline suggestions it'll feel different. Better to know that going in than to waste time figuring out the interaction model while the clock is running.

LinkedIn's AI-enabled round feels like a traditional coding interview with an AI assistant available on the side. The interviewer gives you a problem, you discuss your approach, you implement it, and then follow-up questions push you deeper.

The problems are familiar patterns. LRU caches, interval merging, frequency-based eviction, data transformation. If you've done any amount of coding interview prep, you'll recognize what's being asked. The challenge isn't in figuring out what to do. It's in executing well, managing your time, and being ready for what comes after the initial solution.

Some problems come with predefined method signatures that you need to implement. Others give you raw structured data (think a JSON-like object with employee records or entity information) and ask you to build everything from scratch, classes, methods, tests, all of it. In either case, the interviewer typically wants you to walk through your approach before you start coding.

At Staff level, problems are presented in a design format. Instead of "merge these intervals," you might get addInterval() and insertInterval() methods and need to choose the best data structure for bookkeeping. You're expected to make architectural choices, not just implement a known algorithm.

Don't treat the initial problem as the whole interview. Once you have working code, the interviewer pivots to depth questions that test your engineering maturity.

For cache problems, expect concurrency and multithreading follow-ups. How would you synchronize access? What race conditions could occur? How would you productionize this?

For data processing problems, the follow-ups tend to focus on edge cases you might have missed, whether your solution handles malformed or missing data gracefully, and how it would perform if the dataset grew by orders of magnitude.

These deeper questions carry significant weight. The interviewer is testing whether you understand the implications of what you built, not just whether you can make tests pass.

Based on conversations with candidates who've been through this round, here are the problems we've been able to confirm from multiple independent sources.

LRU Cache. Standard implementation. The problem itself is straightforward if you know the pattern, but the follow-ups are where the interview really happens. Expect questions about concurrency, multithreading, synchronization, edge cases, and production-readiness. One candidate knew the problem well enough that she barely needed the AI for the initial implementation, but the conversation about making it production-ready lasted the rest of the interview.

LFU Cache. Eviction based on ranking, with a mock GetRank() function provided. The follow-up involves implementing LRU eviction as a tiebreaker for elements with the same rank. This was reported at Staff SWE level.

Merge Intervals Variant. Not the classic "given a list of intervals, merge them." Instead, you're given addInterval() and insertInterval() method signatures and need to choose the right data structure for bookkeeping while handling overlapping intervals. Also reported at Staff level with a design-oriented framing.

Structured Data Processing. Given a JSON-like object with entity information (employee data, nested records), build classes, methods, and tests to parse, filter, and sort the results. No starter code or boilerplate. Everything from scratch.

LinkedIn reportedly uses a 4-point grading scale where a 3 is passing, and scoring is relative to other candidates. Here's what the evaluation seems to focus on based on candidate conversations.

Prompt quality. When you use the AI, are your prompts specific and well-structured? Do you give it enough context to get a useful response on the first try, or do you burn time going back and forth? One candidate told me that giving good prompts upfront made everything flow more smoothly because she wasn't wasting cycles on reprompting. Read more about prompt quality and when to use AI in our fundamentals guide.

Verification. This comes up repeatedly. LinkedIn wants to see that you're not blindly accepting AI output. Run the code, check edge cases, and dry-run your solution mentally before moving on. One candidate got an AI response she didn't fully understand. Rather than using it, she told the interviewer "I don't want to use the AI response, I want to write it on my own." The feedback she received afterward was that this was the right call. It's better to write something you understand than to paste something you can't explain. Our guide on verification and testing covers this in detail.

Production thinking. Can you reason about concurrency? Do you know what race conditions look like? Can you discuss how your solution would behave in a real system with real traffic? This gets evaluated in the second half of the interview when the conversation moves past the initial problem, and it's especially important at senior and Staff levels.

Communication. Keep talking to the interviewer throughout. They're there to help, and they want to hear your thought process. One candidate's advice was straightforward. "Keep communicating with the interviewer. They are there to help you." If you're stuck, say so. If you're confused by the AI's response, say so. The interviewer would rather hear you working through the problem out loud than watch you silently struggle. Our guide on communication goes deeper here.

You need to do this. The CoderPad environment with the AI panel is different from coding in your local IDE, and the adjustment period costs real minutes. Practice writing code in CoderPad, using the AI chat, copying responses into the editor, and running tests. Every minute you don't spend fumbling with the UI is a minute you can spend on the actual problem.

If you get a structured data problem, you need to spin up classes, create objects with hardcoded test values, and get a runnable baseline quickly. This is where AI shines and where one candidate I talked to lost his interview. He got stuck trying to parse JSON in Java and burned most of his time before even reaching the logic. The interviewer eventually had to hint that he could just create classes with hardcoded string values instead.

Practice this specifically. Take a JSON object, use AI to create the class structure, hardcode some test data, and get the code running. This should take minutes, not half the interview.

Follow-ups at LinkedIn lean heavily toward production concerns, and concurrency is the most common topic. For cache problems, know how to reason about thread safety, locking strategies, and race conditions. You don't need to implement a fully concurrent solution during the interview, but you need to discuss it intelligently when the interviewer asks.

The interview has a natural rhythm. First, get a working solution. Then, engage with deeper follow-ups about production-readiness and trade-offs. Practice this. Pick a well-known problem, solve it in 20 minutes (with or without AI), then spend 15 minutes thinking about production concerns. What are the edge cases? How would you handle concurrent access? What breaks at scale? Getting comfortable with this rhythm means you won't be caught off guard when the interviewer shifts gears.

The safest approach is to own the logic yourself and use AI for the parts that are tedious but straightforward. Test case generation, boilerplate classes, helper methods you could write but would rather not spend time on. Let AI handle those. For the core algorithm and data structure choices, that should come from you.

If the AI gives you something you don't understand, don't use it. Say "I'm going to write this myself" and move on. Candidates who did this received positive feedback. Candidates who pasted AI code they couldn't explain did not.

Some interviewers will explicitly ask you to write test cases and evaluate your coverage. Even if they don't, writing tests after your solution shows good engineering habits. Use AI to help generate edge case tests quickly. It's a natural use of the tool that also demonstrates judgment about what's worth testing.

If you've gone through LinkedIn's AI-enabled coding interview, drop a comment below. What problem did you get? How did you use the AI? What follow-up questions came up?

If you spot anything outdated here, call it out so I can fix it.

Good luck!