Lead Bank·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Coding round for a software engineer role at Lead Bank. One problem, but it had enough moving parts to keep me busy for a while. The friend recommendation logic was the part that tripped me up.

Questions Asked (1)

You're given a set of edges representing like counts between pairs of users. Implement two things: (a) a query that returns the like count between any two users, and (b) a friend recommendation system. The recommendation works by finding who the target user likes most, then returning that person's most-liked contact. A recommended user can't be the target user themselves, and can't already be a mutual friend. If the top candidate doesn't yield a valid recommendation, fall back to the next-highest liked user and try again. Throw an error if no valid recommendation exists.

Algorithms & Data StructuresSystem Design

Author's notes

The lookup part was fine, basically just a map.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the data model and requirements, then propose an efficient data structure for like counts and a step-by-step algorithm for recommendations. Walk through the algorithm with a concrete example, discussing edge cases and complexity. Finally, mention potential optimizations and trade-offs.

Pro tip: Demonstrate foresight by discussing how to handle ties in like counts and the importance of defining 'mutual friend' precisely. Also, mention that caching or precomputing could improve performance for frequent queries.

1. Clarify Requirements and Assumptions

Ask clarifying questions about the input format, whether edges are directed or undirected, and the definition of 'mutual friend'. Confirm that like counts are between pairs of users and that recommendations should be based on the target user's outgoing likes.

2. Design Data Structures

Propose using a hash map (dictionary) to store like counts between user pairs for O(1) average query time. For recommendations, consider building an adjacency list or priority queue to efficiently find the most-liked users.

3. Outline Recommendation Algorithm

Describe the algorithm: sort the target user's liked users by like count descending. For each candidate, find their most-liked contact (excluding the target and mutual friends). Return the first valid recommendation; if none, throw an error.

4. Analyze Complexity and Edge Cases

Discuss time and space complexity, and address edge cases such as no likes, ties in like counts, cycles, and self-recommendation. Explain how to handle ties (e.g., by user ID or arbitrary order).

5. Discuss Optimizations and Scalability

Mention potential improvements like caching frequent queries, precomputing recommendations, or using more advanced data structures for large-scale systems. Consider trade-offs between query speed and update cost.

Key Points to Mention

Use a hash map for O(1) like count queries, with keys as ordered pairs (user1, user2).
For recommendations, iterate through the target user's liked users in descending order of like count.
Exclude the target user and mutual friends from recommendations; define mutual friend as someone both users like each other.
Handle ties in like counts by consistent ordering (e.g., lexicographically by user ID).
Throw an error if no valid recommendation exists after checking all candidates.
Discuss time complexity: O(L log L + L * M) where L is number of liked users and M is average number of contacts per user.

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