The phone screen had two problems back to back.
Start by clarifying the problem constraints and edge cases, then discuss the brute-force approach and its complexity before optimizing. Implement the optimal solution with clean, modular code, and test with examples including edge cases. If time permits, analyze time and space complexity and suggest further optimizations.
Pro tip: Communicate your thought process continuously, as interviewers assess problem-solving skills and collaboration. If stuck, break the problem into smaller subproblems and solve incrementally.
Ask clarifying questions to confirm input/output formats, constraints, and edge cases. Restate the problem in your own words to ensure alignment.
Discuss a brute-force solution first, then identify bottlenecks and propose optimizations using appropriate data structures or algorithms.
Write clean, modular code with meaningful variable names. Explain your logic as you code, and handle edge cases explicitly.
Walk through your code with a few test cases, including edge cases. If bugs arise, debug systematically by tracing variable states.
State the time and space complexity of your solution. Discuss trade-offs and potential improvements if time allows.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the problem and constraints, then outline your approach before coding. Use the AI assistant to generate boilerplate or suggest optimizations, but critically evaluate its output. Test your solution with edge cases and explain your reasoning throughout.
Pro tip: Treat the AI as a junior pair programmer: delegate repetitive tasks to it, but always review and test its suggestions. This shows you can leverage AI effectively while maintaining code quality.
Restate the problem in your own words and ask clarifying questions about input/output, constraints, and edge cases.
Discuss possible algorithms and data structures, considering time and space complexity. Choose the most efficient one and outline steps.
Use the AI assistant to generate code snippets or suggest optimizations, but review each suggestion for correctness and efficiency.
Walk through your code with sample inputs, including edge cases. Use the AI to help generate test cases if needed.
Discuss potential improvements, trade-offs, and how you would scale the solution. Mention any limitations of your approach.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
System design round went well from my read.
Start by clarifying the requirements and constraints with the interviewer, then outline a high-level design covering the main components and data flow. Dive into critical components, discuss trade-offs, and justify your choices based on scalability, reliability, and performance.
Pro tip: Always begin by asking clarifying questions to scope the problem and align with the interviewer's expectations; this demonstrates senior-level thinking and prevents wasted effort on irrelevant details.
Ask questions to understand functional and non-functional requirements, such as scale, latency, consistency, and availability. Define the scope and constraints.
Sketch the main components (e.g., clients, servers, databases, caches) and how they interact. Identify APIs and data flow.
Select critical components and detail their design, addressing scalability, partitioning, replication, and fault tolerance. Discuss data models and storage choices.
Analyze trade-offs (e.g., SQL vs NoSQL, consistency vs availability) and identify potential bottlenecks. Propose solutions and optimizations.
Summarize the design, reiterate how it meets requirements, and mention future improvements or monitoring.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was more of a conversation than an interrogation, which I appreciated.
Choose a project where you made significant technical decisions with clear trade-offs, and structure your answer to highlight the problem, your decision-making process, and the impact. Emphasize how you balanced technical constraints with cross-functional needs, and quantify results where possible.
Pro tip: Focus on the 'why' behind your decisions, not just the 'what'—interviewers at LinkedIn value engineers who can articulate trade-offs and align technical choices with business goals. Also, briefly mention how you communicated these decisions to non-technical stakeholders to demonstrate cross-functional alignment.
Briefly describe the project, your role, and the team's objective. Keep it concise to save time for the technical details.
Explain the key technical problem or decision point, including constraints such as scalability, latency, or resource limitations.
Outline the alternatives you considered and the pros and cons of each. Show how you evaluated them against technical and business criteria.
Describe the chosen solution, why it was the best fit, and how you implemented it. Mention any cross-functional collaboration that influenced the decision.
Share the measurable impact (e.g., performance improvements, cost savings) and what you learned. Reflect on what you might do differently.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Structure your answer as a concise narrative that connects your past roles to the specific needs of the Software Engineer role at LinkedIn. Highlight experiences where you navigated ambiguity and adapted quickly, using concrete examples and metrics to demonstrate impact.
Pro tip: Research LinkedIn's engineering culture and recent projects, then tailor your background to show how your adaptability and problem-solving skills align with their focus on innovation and scale.
Start with a brief overview of your current role and total years of experience, then state your interest in LinkedIn and this position.
Walk through 2-3 key roles chronologically, focusing on responsibilities and technologies that match the job description.
Highlight specific instances where you faced unclear requirements or changing priorities, and explain how you navigated them successfully.
Quantify your achievements with metrics (e.g., performance improvements, user growth) to demonstrate your value.
Conclude by linking your background to LinkedIn's mission and engineering challenges, expressing enthusiasm for contributing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.