Coinbase·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

May 2026Remote

Summary

Coinbase SWE interview with a meaty algorithmic design problem around NFT metadata generation. The question had multiple layers of constraints stacked on top of each other, which kept things interesting but also stressful.

Questions Asked (1)

Given a set of trait types (like background, eyes, hat), each with a list of valid values, and a target count N, write a function to generate N NFT metadata items. Then handle follow-up constraints: no repeated trait value within a single NFT, uniqueness of the full trait combination across all generated NFTs, and optional inclusion/exclusion rules between traits. Also explain your data structures, algorithm, complexity, and how you'd test it.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

This one had me feeling good for about two minutes and then the follow-ups started piling up.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and edge cases, then design a data model and algorithm that ensures uniqueness and respects constraints. Explain your approach step-by-step, covering data structures, complexity, and testing strategy. Emphasize trade-offs and scalability.

Pro tip: Mention using a set for O(1) uniqueness checks and a backtracking approach for constraint satisfaction, but also discuss probabilistic methods like rejection sampling for efficiency. Show awareness of potential infinite loops and how to handle them.

1. Clarify Requirements and Edge Cases

Ask about constraints: can traits be optional? What if N exceeds possible unique combinations? How to handle inclusion/exclusion rules? Clarify output format.

2. Design Data Structures

Represent traits as a dictionary mapping trait type to list of values. Use a set to store generated combinations for O(1) uniqueness checks. For inclusion/exclusion, consider a graph or adjacency list.

3. Develop Generation Algorithm

For each NFT, randomly select one value per trait, ensuring no duplicate values within the NFT. Check uniqueness of the full combination; if duplicate, retry. For inclusion/exclusion, enforce rules during selection or post-validation.

4. Analyze Complexity and Trade-offs

Discuss time complexity: O(N * T) for generation, but retries can increase it. Space complexity: O(N) for storing combinations. Compare with deterministic generation using combinatorial enumeration.

5. Outline Testing Strategy

Test with small N and known combinations, edge cases (N=0, N=max possible), constraint violations, and performance with large N. Use unit tests for each constraint and property-based testing for randomness.

Key Points to Mention

Use of set for O(1) uniqueness checks of full trait combinations.
Handling of inclusion/exclusion rules via constraint propagation or post-generation filtering.
Complexity analysis: best/worst-case time and space, and impact of retries.
Trade-offs between deterministic and probabilistic generation methods.
Testing strategies: unit tests, edge cases, and property-based testing.
Scalability considerations for large N and many traits.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.