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 12 problems of 25
- #3mediumfrequently asked
3. Longest Substring Without Repeating Characters
Find the length of the longest substring with all unique characters. Hugging Face asks this to test sliding-window thinking — the same pattern used to scan a fixed-size context window over a token sequence during chunked inference on long documents.
- #15mediumfrequently asked
15. 3Sum
Find all unique triplets in an array that sum to zero. Hugging Face uses 3Sum to test whether candidates can extend two-pointer reasoning — a skill that maps directly to efficiently scanning attention weight triplets or ranking triple-wise loss computations in metric-learning models.
- #49mediumfrequently asked
49. Group Anagrams
Group strings that are anagrams of each other. Hugging Face uses this to test canonical-key hashing — the same fingerprinting technique used to cluster semantically equivalent dataset examples or deduplicate near-duplicate model cards in the Hub.
- #56mediumfrequently asked
56. Merge Intervals
Merge all overlapping intervals into the fewest possible. Hugging Face uses this to test sort-then-sweep reasoning — the same pattern used when merging overlapping token spans from multiple annotators or collapsing overlapping time ranges in a batch inference log.
- #139mediumfrequently asked
139. Word Break
Determine if a string can be segmented into words from a dictionary. Hugging Face uses this as a DP signal problem with direct ML relevance — tokenization itself is a form of word breaking, and understanding how dynamic programming explores segmentation space helps engineers reason about subword tokenizer design.
- #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.
- #200mediumfrequently asked
200. Number of Islands
Count connected components of '1's in a binary grid. Hugging Face uses this BFS/DFS graph traversal problem to probe spatial reasoning — the same connected-component logic used to identify coherent token clusters in attention heat maps or connected regions in image-segmentation datasets.
- #207mediumfrequently asked
207. Course Schedule
Detect whether all courses can be finished given prerequisite constraints — a cycle detection problem on a directed graph. Hugging Face uses this to test DAG reasoning, which applies directly to dependency resolution in ML pipeline DAGs where a circular dependency between data preprocessing and model training steps would deadlock the entire run.
- #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.
- #238mediumfrequently asked
238. Product of Array Except Self
Compute for each index the product of all other elements without using division. Hugging Face uses this to test prefix/suffix scan thinking — the same forward-backward pass pattern that computes gradient accumulation across a sequence in backpropagation through time.
- #322mediumfrequently asked
322. Coin Change
Find the minimum number of coins to make a target amount. Hugging Face uses this unbounded knapsack DP to assess whether candidates can formulate and optimize a recurrence — the same skill needed to tune beam search width or minimize inference steps in iterative decoding strategies.
- #347mediumfrequently asked
347. Top K Frequent Elements
Return the k most frequent elements in an array. Hugging Face uses this to probe heap vs. bucket sort thinking — directly analogous to computing the top-k tokens by log probability during beam search or finding the most frequent terms in a large text dataset.