After five weeks and eight interview rounds, my LinkedIn SDE interview process finally came to an end.
Before going in, I read quite a few interview experiences online, but very few covered the full eight-round process. So I tried to document each round as clearly as possible.
My biggest takeaway is this: LinkedIn is not a company that simply filters candidates based on how quickly they can solve LeetCode problems. The early rounds definitely include coding, but starting around the fourth round, technical depth, project judgment, and product thinking become much more important.
Your ability to explain the systems you have actually built — including the trade-offs, mistakes, bottlenecks, and decisions behind them — matters much more than instantly finding the optimal solution.
Interview Process Overview
| Round | Interview Type | Core Focus |
|---|---|---|
| 1 | Recruiter Call | Why LinkedIn, product understanding, metrics |
| 2 | Phone Screen | Project discussion, behavioral questions, minimum workers problem |
| 3 | Coding | Currency conversion using graphs and BFS |
| 4 | Tech Lead Interview | Deep project dive and high-density behavioral questions |
| 5 | LLM + Coding | RAG, hallucinations, and longest path in a DAG |
| 6 | System Design | Professional social platform and Feed architecture |
| 7 | Project Deep Dive | Payment gateway, idempotency, and reconciliation |
| 8 | Hiring Manager Interview | Technical follow-ups and culture fit |
Round 1: Recruiter Call
This round lasted around 30 minutes. It felt much more structured than a casual conversation.
Why LinkedIn?
This is worth preparing properly. Saying something like "I want to work at a big tech company" probably will not leave much of an impression.
A stronger answer connects your interests to LinkedIn's actual engineering challenges. For example:
I am particularly interested in recommendation and Feed ranking systems, and LinkedIn's scale and data density make it an interesting environment for building products around professional relationships and career growth.
Product Understanding
The recruiter also asked about LinkedIn's product and business model. It is useful to understand the company's major business areas, including:
- Talent Solutions
- Marketing Solutions
- Premium Subscriptions
Simply describing LinkedIn as "a platform for finding jobs" is far too shallow. Understanding how the product creates value and how success is measured helps throughout the entire interview process.
Round 2: Phone Screen — Project + Behavioral + Coding
This round was conducted through CoderPad and lasted about 60 minutes.
The first 15 minutes focused on project discussion and behavioral questions, followed by a coding problem.
Behavioral Question
I was asked to describe a time when I received negative feedback and how I responded.
This theme appeared again in later rounds. The interviewer seemed more interested in your actual judgment and actions than in a perfectly polished story about how grateful you were for feedback.
Coding: Minimum Number of Workers
Given the start and end times of multiple tasks, find the minimum number of workers required to process them.
The standard solution was to convert each interval into two events:
- Task starts:
+1 - Task ends:
-1
After sorting the events by time, scan through them and track the maximum number of concurrent tasks.
The interviewer then asked several follow-up questions:
- If one task ends exactly when another begins, can the same worker be reused?
- Can one worker process multiple tasks at the same time?
The main lesson here was to clarify boundary conditions proactively instead of assuming them.
Round 3: Coding — Currency Conversion
This round was entirely focused on coding.
The problem modeled currencies as graph nodes and exchange rates as directed edges. Given a source and target currency, the goal was to find a valid conversion path and multiply the exchange rates along that path.
A straightforward BFS or DFS works well for a single query.
Possible Follow-Ups
- Cache frequently queried currency pairs
- Use graph connectivity techniques for reachability checks
- Handle floating-point precision issues carefully
- Support reverse exchange rates
- Consider cycles in the graph
If numerical stability becomes important, logarithmic transformations can also be useful depending on the problem constraints.
Round 4: Tech Lead Interview — Project Depth + Behavioral Questions
This was probably the highest-density interview in the entire process.
The first 20 minutes focused on a deep dive into one of my projects. The remaining 40 minutes were almost entirely behavioral questions.
Project Deep Dive
The interviewer asked questions such as:
- Why did you choose this technology stack?
- What alternatives did you consider?
- Where were the performance bottlenecks?
- How did you profile the system?
- If you rebuilt it today, what would you change?
Simply explaining why every decision was correct is not enough. A strong answer should explain the reasoning behind the decision and acknowledge what you did not know at the time.
Being honest about trade-offs usually works better than trying to defend every past decision.
Common Behavioral Questions
- Tell me about a conflict you had with someone.
- What was your most challenging project, and what would you change if you did it again?
- Tell me about a time when you had to learn a new technology quickly.
- Why did something pass local testing but still fail in production?
- How do you deal with ambiguous requirements?
For conflict questions, a weak answer is:
The other person simply did not understand the system.
A much stronger answer focuses on how the disagreement was resolved:
We realized that we were optimizing for different risks. We wrote down the possible failure scenarios, compared their potential impact, and ran a small-scale validation before making the final decision.
That demonstrates engineering judgment instead of simply assigning blame.
Round 5: LLM + Coding
LLM Discussion
The discussion covered several practical LLM topics:
- RAG vs. fine-tuning
- Common causes of hallucinations
- Ways to reduce hallucinations
Potential approaches included retrieval augmentation, temperature tuning, citation constraints, and human validation.
One interesting follow-up was about the downside of setting the temperature too low. Lower temperature can make responses more repetitive and conservative, which may reduce quality for open-ended tasks.
Coding: Longest Dependency Chain in a DAG
The coding problem involved finding the longest dependency chain in a directed acyclic graph.
A typical solution combines:
- Topological sorting
- Dynamic programming
Follow-up questions included:
- How would you handle weighted edges?
- What happens if the graph contains multiple disconnected components?
A properly implemented graph traversal naturally handles disconnected components.
Round 6: System Design — Designing a Professional Social Platform
The system needed to support:
- User profiles
- Social relationships
- Posts
- Personalized feeds
I started by clarifying several important requirements:
- DAU and overall scale
- Read-to-write ratio
- Real-time requirements
- One-way vs. two-way relationships
Possible Storage Design
- User profiles: relational database such as MySQL
-
Social graph: sharded by
user_id - Posts: write-heavy, time-ordered storage such as Cassandra
Feed Architecture
The most important design decision was the Feed generation strategy.
A hybrid push-and-pull model works well:
- Normal users: fan-out on write
- High-follower or celebrity accounts: fan-out on read
A Redis Sorted Set can be used to maintain time-ordered Feed entries for fast retrieval.
The interviewer also asked about hot users and extreme relationship growth. For example, if someone follows hundreds of thousands of users, generating their Feed cannot simply rely on a naive fan-out strategy.
You need to discuss concrete mitigation strategies rather than stopping at a generic answer like "we can add more servers."
Round 7: Project Deep Dive — Payment Gateway
This round focused on a payment system I had worked on.
The discussion covered:
- Payment channel abstraction
- Transaction state machines
- Webhook processing
- Reconciliation workflows
Duplicate Callbacks
The solution was to make processing idempotent. A channel transaction ID could be stored with a unique constraint to prevent duplicate processing.
Missing Callbacks
For payments stuck in a PROCESSING state:
- Periodically query the payment provider.
- Update the transaction state if the external result is available.
- Retry within a defined limit.
- Move transactions that exceed the retry threshold into a manual review queue.
The important concept here is eventual consistency. Distributed payment workflows should not assume that every callback will arrive exactly once and succeed immediately.
Round 8: Hiring Manager Interview
The technical discussion continued from previous rounds, especially around performance bottleneck analysis.
Topics included profiling tools and approaches such as:
- Flame graphs
- async-profiler
- CPU and memory profiling
Legacy System Scenario
A classic scenario was:
You inherit a slow legacy system with poor test coverage and limited monitoring. What would you do?
The answer I gave followed this general approach:
- Improve observability first.
- Use production data to identify the highest-impact bottlenecks.
- Make incremental improvements.
- Avoid attempting a massive rewrite before understanding the actual problem.
The culture-fit discussion also returned to conflict resolution and product thinking.
I was asked something along the lines of:
If you joined LinkedIn as an engineer, what area would you most want to invest in?
Topics such as recommendation cold starts, real-time content moderation, and Feed quality can all lead to interesting discussions if you connect them to real product problems.
How I Would Prepare for LinkedIn
1. Understand the Product
Learn the major business lines and important product metrics. Do not treat LinkedIn as simply a job-search website.
2. Prepare for Deep Project Discussions
You should be able to explain:
- Why you chose a particular architecture
- What alternatives you considered
- Performance bottlenecks and actual numbers
- How you profiled the system
- What you would change if you rebuilt it
- What would break if traffic increased by 10×
3. Prepare Behavioral Stories Beyond the First Answer
Conflict, negative feedback, and ambiguous requirements are all worth preparing for multiple layers of follow-up questions.
4. Focus on Practical Coding Patterns
Topics worth reviewing include:
- Interval scheduling
- Graphs
- Currency conversion problems
- DAGs
- Top-K problems
Most questions felt closer to Medium difficulty. Clear reasoning and high-quality implementation mattered more than solving extremely difficult problems.
5. Practice Feed System Design
The push-vs.-pull trade-off is particularly important. Be prepared to discuss hot users, fan-out costs, storage, caching, and scalability.
Final Thoughts
Coding mainly dominated the first few rounds. The biggest differentiator came later.
The candidates who stand out are usually not just the ones who can solve algorithms quickly. They can explain their projects deeply, understand how engineering decisions affect products, and demonstrate real judgment when dealing with trade-offs and conflicts.
If you only prepare by grinding LeetCode, the later LinkedIn rounds may feel surprisingly uncomfortable.
Personally, I think spending time deeply understanding your most complex project — along with Feed architecture, payment idempotency, and LinkedIn's product logic — is more valuable than solving another hundred random problems.
If you are preparing for LinkedIn or other major tech companies, it can also be helpful to practice project deep dives, Feed system design, and payment system fundamentals separately.
InterviewShow provides interview preparation support for companies such as LinkedIn, Google, and Meta, with one-on-one guidance throughout the preparation process.
Good luck with your interviews — hope you get the offer you're aiming for.
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