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.
- #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.
- #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.
- #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.
- #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.
- #20easyfrequently asked
20. Valid Parentheses
Determine whether a string of brackets is valid. Hugging Face uses this to probe stack intuition — the same LIFO discipline that governs recursive transformer decoder calls and nested tokenization schemas in production ML systems.
- #21easyfrequently asked
21. Merge Two Sorted Lists
Merge two sorted linked lists into one sorted list. Hugging Face uses this to test the merge step of merge sort — a fundamental primitive for combining ranked model outputs or merging sorted inference result streams in distributed ML serving pipelines.
- #23hardfrequently asked
23. Merge K Sorted Lists
Merge k sorted linked lists into one sorted list efficiently. Hugging Face uses this to assess whether candidates can compose primitives (min-heap, divide-and-conquer) for distributed inference — the same pattern used when merging ranked result streams from multiple model shards serving parallel requests.
- #42hardfrequently asked
42. Trapping Rain Water
Calculate total water trapped between elevation bars. Hugging Face uses this to test multi-approach fluency — brute force, prefix-max arrays, and two-pointer — the same progression used when optimizing a naive inference pass to a streaming one-shot scan for ML feature extraction pipelines.
- #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.
- #53easyfrequently asked
53. Maximum Subarray
Find the contiguous subarray with the largest sum. Hugging Face uses Kadane's algorithm as a litmus test for greedy DP thinking — the same pattern used when identifying the highest-scoring span in extractive question-answering models.
- #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.
- #70easyfrequently asked
70. Climbing Stairs
Count the distinct ways to climb n stairs taking 1 or 2 steps at a time. Hugging Face uses this as a gateway to dynamic programming — the same recurrence thinking that underlies sequence-to-sequence decoding where each output token depends on a bounded window of prior states.
- #121easyfrequently asked
121. Best Time to Buy and Sell Stock
Find the maximum profit from a single buy-sell transaction. Hugging Face uses this to assess greedy one-pass thinking — the same mindset needed to efficiently scan token log-probability arrays during beam search without re-processing elements.
- #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.
- #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.
- #206easyfrequently asked
206. Reverse Linked List
Reverse a singly linked list in-place. Hugging Face asks this to confirm pointer manipulation fluency — a prerequisite for understanding the attention mask and key-value cache pointer operations that arise in transformer serving optimizations.
- #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.
- #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.
- #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.
- #642hardfrequently asked
642. Design Search Autocomplete System
Design a search autocomplete system that ranks completions by historical frequency. Hugging Face asks this because it directly mirrors their Hub model search infrastructure — prefix matching on model names with ranking by download counts requires exactly the Trie + frequency heap composition tested here.