Choose a project where you can clearly articulate the problem, constraints, and trade-offs that drove the design. Focus on explaining why the team made specific architectural or technical decisions, not just what was built. Highlight your individual contribution and how you navigated ambiguity.
Pro tip: Emphasize the constraints (e.g., latency, scale, cost) that forced trade-offs, and show you understand the 'why' behind decisions even if you didn't make them. This demonstrates systems thinking and maturity beyond just coding.
Briefly describe the project, your role, and the team's goal. Mention the scale and any key constraints (e.g., latency, throughput, budget).
Outline the core problem the service solved and the functional and non-functional requirements (e.g., scalability, reliability, cost).
Walk through the chosen architecture and the alternatives considered. Explain why the team chose this design over others, referencing trade-offs.
Detail what you personally worked on, any obstacles you faced, and how you resolved them. Show adaptability and ownership.
Share the results (e.g., performance improvements, user impact) and what you learned about system design and teamwork.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Was not expecting Hard difficulty for a first round screen.
Start by clarifying the problem constraints and edge cases, then discuss brute force and optimize using appropriate data structures and algorithms. Think aloud, write clean code, and test with examples to demonstrate problem-solving and communication skills.
Pro tip: At TikTok, interviewers value efficient, scalable solutions and clear communication. Always analyze time and space complexity and discuss potential optimizations, even if you don't implement them.
Ask clarifying questions to confirm input/output, 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 optimized approaches using suitable data structures or algorithms.
Outline your chosen approach step-by-step, then write clean, modular code while explaining your thought process.
Walk through your code with sample inputs, including edge cases, and fix any issues. Verify correctness and efficiency.
State the time and space complexity of your solution and discuss potential improvements or trade-offs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.