Character AI·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Character AI coding round, pretty much one meaty question about building a HashMap from scratch. They wanted the full picture: implementation, tradeoffs, complexity. Felt like a solid mid-level systems-flavored coding problem.

Questions Asked (1)

Design and implement a HashMap from scratch without using any built-in hash table libraries. Your implementation should support put, get, and remove operations. Walk through your design choices including collision handling, hash function selection, load factor management, and dynamic resizing, and analyze the time and space complexity.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

I went with separate chaining first since it felt safer to explain, bucket array with a linked list at each slot.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then outline the core components: hash function, collision resolution, load factor, and resizing. Walk through the implementation step-by-step, explaining design choices and analyzing time/space complexity for each operation.

Pro tip: Discuss trade-offs between different collision resolution strategies (e.g., chaining vs. open addressing) and justify your choice based on expected workload and performance goals. Mention how you would handle edge cases like null keys and high collision rates.

1. Clarify Requirements and Constraints

Ask about expected key/value types, performance requirements, and whether thread safety is needed. Confirm that no built-in hash table libraries can be used.

2. Design Core Components

Choose a hash function (e.g., polynomial rolling hash for strings), collision resolution strategy (e.g., separate chaining with linked lists or open addressing with linear probing), and initial capacity/load factor.

3. Implement Operations

Code put, get, and remove. For put, compute hash, find bucket, handle collisions, and insert/update. For get, compute hash and search bucket. For remove, compute hash, find and delete entry.

4. Manage Load Factor and Resizing

Track number of entries and resize (e.g., double capacity) when load factor exceeds threshold (e.g., 0.75). Rehash all existing entries into the new bucket array.

5. Analyze Complexity and Trade-offs

Discuss average O(1) time for operations with good hash function and low load factor, worst-case O(n) with many collisions. Space complexity O(n). Compare chaining vs. open addressing.

Key Points to Mention

Hash function selection: aim for uniform distribution, consider key types (e.g., strings, integers), and use techniques like modulo a prime number.
Collision handling: separate chaining (linked lists or balanced trees) vs. open addressing (linear/quadratic probing, double hashing), with pros and cons.
Load factor management: define threshold (e.g., 0.75), monitor size/capacity ratio, and trigger resizing to maintain performance.
Dynamic resizing: double capacity, rehash all entries, and amortize cost over operations; discuss why doubling leads to amortized O(1).
Time complexity: average O(1) for put/get/remove, worst-case O(n) due to collisions; space complexity O(n).
Edge cases: null keys, duplicate keys, high collision scenarios, and resizing during iteration.

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