Hugging Face Coding Interview Questions
25 Hugging Face coding interview problems with full optimal solutions — 8 easy, 12 medium, 5 hard. Every problem ships with multiple approaches (brute-force first, then the optimal), complexity tables for each, company-specific tips on what an Hugging Face interviewer values, and a FAQ section.
Showing 6 problems of 25
- #1easyvery frequently asked
1. Two Sum
Find two numbers in an array that add up to a target. Hugging Face uses this as a warm-up to test whether candidates think in hash maps — the same O(1) lookup mindset that underlies efficient tokenizer vocabulary lookups in ML pipelines.
- #4hardsometimes asked
4. Median of Two Sorted Arrays
Find the median of two sorted arrays in O(log(m+n)) time. Hugging Face uses this to test binary search on abstract search spaces — a skill that transfers to efficiently finding threshold values in calibration curves for ML model confidence scoring.
- #127hardsometimes asked
127. Word Ladder
Find the shortest word transformation sequence from begin to end using a dictionary. Hugging Face uses this BFS shortest-path problem to probe graph construction from implicit edges — the same skill needed to build token neighborhood graphs for nearest-neighbor search in embedding spaces.
- #146mediumvery frequently asked
146. LRU Cache
Design a cache that evicts the least-recently-used entry when full. Hugging Face uses this because it mirrors a real problem they solve at scale — caching tokenizer outputs and hosted model inference results where stale entries must be evicted under memory pressure to serve millions of API requests efficiently.
- #208mediumvery frequently asked
208. Implement Trie (Prefix Tree)
Build a prefix tree that supports insert, search, and startsWith. Hugging Face uses this because a Trie is the canonical data structure for prefix-based token lookup in tokenizer vocabularies — understanding it deeply signals you can reason about the internals of text processing infrastructure.
- #217easysometimes asked
217. Contains Duplicate
Return true if any value appears at least twice in an array. Hugging Face uses this as a hash-set baseline — the same deduplication logic that filters repeated tokens, removes duplicate dataset examples, and deduplicates model card identifiers in the Hub registry.