LinkedIn's software engineer loop is a recruiter call, one technical phone screen, and an onsite of five to six rounds that mixes coding, system design, and behavioral questions. The process starts with a 30-minute recruiter phone call to discuss your resume and motivations, and candidates who pass the technical phone screen are invited to an onsite that usually totals around five hours. The thing that trips people up is not the difficulty of any single problem. It is that LinkedIn's coding rounds reward clean, fully working code and clear communication over clever one-liners, and the process is centralized in a way that changes how you should prep.
The interview process, stage by stage
Here is the shape of the loop for most software engineer roles:
| Stage | Format | What they check |
|---|---|---|
| Recruiter screen | ~30 min call | Background, motivation, logistics |
| Technical phone screen | ~45-60 min, shared editor | One or two coding problems |
| Onsite | 5-6 rounds | Coding, system design, behavioral |
The recruiter call is a fit and logistics conversation, not a technical gate. Use it to confirm which track you are on (backend, mobile, SRE, frontend) and the team, because that determines whether you get a domain-specific round.
The phone screen is a live coding round in a shared editor. This interview will generally be one question or sometimes two, depending on how quickly you work through the first one. Expect medium difficulty and expect to run your code against edge cases.
The onsite is where the volume is. You participate in five to six interviews, with about three focused on coding (data structure and algorithm questions). You will also typically have one to three system design interviews, and in both coding and system design rounds you should be prepared for a few behavioral questions. There is usually a manager conversation focused on behavioral topics as well.
One structural detail matters a lot. Because LinkedIn's interview process is centralized, you generally won't be interviewing with engineers or managers from the team you'll end up on. Team matching happens separately. That means you cannot lean on team-specific rapport, and you should treat every interviewer as a neutral evaluator scoring you against a rubric.
How LinkedIn scores you
LinkedIn uses a numeric rubric, not vibes. Each onsite round is graded on a 4-point scale, where 3 is passing. If your aggregate score is borderline, you may be asked to do another interview, and candidates who end up over the line get passed to the hiring committee, which decides on both hiring and leveling.
Two takeaways. First, a single weak round will not sink you if the rest are strong, so recover and keep going. Second, leveling is decided by committee after the loop, which is why some candidates get bumped up or down from the level they applied for. If you are aiming for senior or staff, the depth of your system design and the scope in your behavioral stories are what move the level.
Coding: what actually gets asked
LinkedIn's coding questions skew toward strings, arrays, hash tables, and clean edge-case handling. Based on the problems we see asked most often at LinkedIn in the DevInterview question bank, here is the practical priority order:
- Longest Substring Without Repeating Characters (mid/senior): the canonical sliding-window problem. If you cannot write this cleanly with a hash map tracking last-seen indices, start here.
- Longest Palindromic Substring (mid/senior): expand-around-center is the expected approach. Interviewers watch whether you handle even and odd length centers.
- Median of Two Sorted Arrays (staff): the hard binary-search-on-partition problem. This shows up for higher levels and is a common signal for staff loops.
- Letter Combinations of a Phone Number (mid/senior): backtracking with a digit-to-letter map. Good for testing recursion clarity.
- Reverse Integer and Integer to Roman / Roman to Integer: math and simulation questions where the whole point is overflow handling and mapping correctness, not algorithmic depth.
- Two Sum (junior): still appears as a warm-up or phone screen opener. Do not overthink it, but do state the hash-map tradeoff out loud.
The pattern here is clear. LinkedIn cares more about whether you write correct, readable code and reason about edge cases than whether you know an obscure algorithm. Reverse Integer and the Roman numeral problems are not hard; they are traps for people who skip the boundary conditions. Practice narrating your edge cases before you code them.
If you want to drill the underlying patterns, the highest-leverage concepts for LinkedIn are strings, hash tables, and two pointers, plus binary search if you are targeting staff. You can filter the full LinkedIn set on our company breakdown.
A concrete prep plan
We run mock interviews all day, and the failure mode we see most for LinkedIn candidates is under-practicing the explanation, not the algorithm. A workable four-week plan:
- Weeks 1-2: solve the sliding-window, backtracking, and math problems above until you can code each in under 20 minutes while talking. Force yourself to dictate the approach before typing.
- Week 3: system design. Practice designing LinkedIn-flavored systems (a feed, a connection graph, a notification service). The system design round asks you to create a high-level design for a product, for instance LinkedIn from scratch, a recommendation engine, or an improvement to an existing product.
- Week 4: full mock loops back to back, including behavioral, to build the stamina a five-hour onsite demands.
System design and behavioral
For system design, expect general large-scale system questions rather than narrow trivia. Cover the standard toolkit: load balancing, sharding, caching, replication, and the read-versus-write tradeoffs of a social graph. Drive the conversation, state assumptions, and quantify with back-of-the-envelope numbers before you draw boxes.
Behavioral questions are woven through the loop, not isolated to one round. LinkedIn interviewers probe collaboration, ownership, and how you handle disagreement, so bring three or four stories structured cleanly and rehearsed enough that you can adapt them on the fly. Our guide on the STAR method for software engineers is a good scaffold: keep the situation short and spend your words on the action and the measurable result.
If you have interviewed at other big-tech companies, note the contrast. LinkedIn feels closer to Microsoft in tone than to the high-speed two-questions-in-45-minutes pace of Amazon. The bar is correctness and clarity, and interviewers give you room to think out loud.
FAQ
How hard are LinkedIn coding interviews?
Medium on average, with occasional hard problems (Median of Two Sorted Arrays shows up for senior and staff loops). The difficulty is less about exotic algorithms and more about writing bug-free code and handling edge cases under time pressure. Reverse Integer and Roman numeral conversions look easy but punish sloppy boundary handling.
How many coding rounds does LinkedIn have?
Usually about three coding rounds in the onsite, plus the technical phone screen. The onsite is five to six interviews with roughly three focused on coding and one to three on system design. Niche roles like mobile or SRE add a domain-specific round.
Do I interview with my future team at LinkedIn?
Generally no. The process is centralized, so you usually won't interview with engineers or managers from the team you'll end up on. Team matching happens after you pass, which means you should treat the loop as a neutral, rubric-based evaluation rather than a team fit chat.
What score do I need to pass?
Each round is graded on a 4-point scale where 3 is passing, and a borderline aggregate may trigger an additional interview. The hiring committee then decides both the hire and your level, so strong system design and high-scope behavioral answers directly affect your offer level.
How long is the LinkedIn onsite?
It usually totals around five hours across the rounds. Plan your energy accordingly and treat the last round as seriously as the first, since a single weak round can still be offset by strong performance elsewhere.