Pretty standard opener but I always underestimate how much they actually dig in.
Structure your resume walkthrough as a narrative that highlights your adaptability and ability to deliver results in ambiguous situations, tailored to Samsung's software engineering needs. Focus on 2-3 key experiences that demonstrate technical depth, cross-functional collaboration, and learning agility, connecting each to the role's requirements.
Pro tip: Quantify your impact and explicitly state how you navigated ambiguity or changing requirements; Samsung values engineers who can drive clarity and innovate under uncertainty.
Start with a concise summary of your current role and years of experience, setting the stage for your narrative.
Select 2-3 roles or projects that showcase your technical skills and adaptability, especially those involving ambiguous or evolving requirements.
For each experience, describe the situation, the ambiguity or challenge, and how you adapted to deliver results.
Relate your experiences to Samsung's projects, technologies, or culture, showing how you can contribute to their software engineering team.
Summarize your interest in the role and how your background aligns with Samsung's goals, inviting further discussion.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Came up in both rounds in different forms.
Start by clarifying the requirements and constraints (e.g., consumers, scale, latency, security) before proposing any design. Then walk through a structured API design process: resource modeling, endpoint design, versioning, error handling, and security. Finally, discuss trade-offs and how you would validate the design with stakeholders.
Pro tip: Demonstrate awareness of Samsung's device ecosystem and backend constraints by mentioning how your API design would support multiple device types, handle intermittent connectivity, and ensure backward compatibility for long-lived products.
Ask questions to understand the use case, expected load, latency requirements, security needs, and existing systems. This shows you don't jump to solutions prematurely.
Identify the core entities and their relationships. Use RESTful principles or GraphQL based on the use case, and define clear resource hierarchies.
Define URIs, HTTP methods, request/response schemas, status codes, and error formats. Ensure consistency and idempotency where needed.
Cover authentication, authorization, rate limiting, versioning, pagination, caching, and monitoring. Explain how these are implemented.
Compare alternatives (e.g., REST vs. gRPC), explain your choices, and outline how the API will evolve without breaking clients.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Felt fine during it but you never really know.
Start by clarifying the problem constraints and edge cases, then discuss a brute-force solution before optimizing with appropriate data structures. Implement the optimized solution cleanly, test with examples, and analyze time/space complexity.
Pro tip: Verbally walk through your thought process as you code, and proactively test edge cases like empty input or large values—interviewers value clear communication and thoroughness over silent perfection.
Restate the problem in your own words and ask clarifying questions about input size, constraints, and expected output format. Confirm edge cases like empty input or duplicates.
Discuss a brute-force solution first, then propose optimizations using appropriate data structures or algorithms. Compare trade-offs in time and space complexity.
Outline the steps of your chosen approach in pseudocode or comments before writing actual code. This reduces errors and shows structured thinking.
Write clean, modular code with meaningful variable names. Test with the provided examples and additional edge cases, debugging as needed.
State the time and space complexity of your solution. If time permits, discuss potential further optimizations or alternative approaches.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.