Coinbase·Software Engineer·Technical Phone Screen·Senior

Senior

Apr 2026Remote

Summary

Coinbase technical screen that was basically one meaty algorithm design problem about weighted random sampling for NFT generation. Not a grind-leetcode type of question, more of a systems-thinking-meets-probability thing that I wasn't fully prepared for.

Questions Asked (1)

You have a set of NFT attribute categories (like ear shape or background), each with multiple possible values that have their own probabilities. Design an algorithm to randomly sample NFTs that respects those per-attribute weights, and discuss how you'd handle deduplication if every generated NFT needs to be unique.

Algorithms & Data StructuresTechnical Trade-offsSystem Design

Author's notes

I started with the cumulative sum plus binary search approach because it felt safe and I could explain the math clearly.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements: independent per-attribute sampling, uniqueness constraint, and scale. Then describe a weighted random sampling method (e.g., cumulative distribution or alias method) for each attribute, and discuss deduplication strategies like storing generated NFTs in a hash set and resampling on collision, or using a deterministic generation with a permutation to avoid collisions. Finally, analyze trade-offs between approaches in terms of time, space, and feasibility.

Pro tip: Mention the birthday paradox to estimate collision probability and suggest a hybrid approach: pre-generate all possible combinations if the space is small, or use a Bloom filter for efficient deduplication at scale.

1. Clarify Requirements and Constraints

Ask about the number of attributes, values per attribute, desired NFT count, and whether the total combination space is known. This determines if the problem is tractable and guides algorithm choice.

2. Design Weighted Sampling per Attribute

For each attribute, use a weighted random selection algorithm such as cumulative distribution with binary search or the alias method for O(1) sampling. Ensure independence across attributes.

3. Handle Uniqueness and Deduplication

Propose storing generated NFTs in a hash set for O(1) lookup. On collision, resample. Discuss alternatives like generating all combinations and shuffling if space is small, or using a deterministic index mapping.

4. Analyze Complexity and Trade-offs

Compare time and space complexity of approaches. Consider collision probability (birthday paradox) and scalability. Discuss when resampling becomes inefficient and alternatives like pre-generation or Bloom filters.

5. Discuss Edge Cases and Optimizations

Address cases where the number of unique NFTs requested exceeds the total possible combinations. Suggest optimizations like caching weighted distributions or parallel generation with synchronization.

Key Points to Mention

Weighted random sampling techniques: cumulative distribution, alias method, or rejection sampling.
Uniqueness enforcement: hash set for deduplication, resampling on collision, and its expected time complexity.
Birthday paradox to estimate collision probability and its impact on performance.
Alternative approaches when combination space is small: pre-generate all combinations and shuffle.
Scalability considerations: distributed generation, Bloom filters for approximate deduplication, or deterministic generation using a permutation.
Trade-offs between time, space, and feasibility, especially when requested NFTs exceed total possible combinations.

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