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 8 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.
- #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.
- #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.
- #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.
- #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.
- #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.