Hugging Face Coding & DSA interview questions

Batching inputs is how you keep a GPU fed, but the source is an iterator you cannot index or len(). The warm-up that screens whether you can write a lazy generator that handles the ragged final batch.

Every chat model decodes with top-p, but few candidates can build it from logits: softmax, sort, take the smallest set of tokens whose cumulative probability crosses p, renormalize, sample. The screen for whether you understand decoding, not just call an API.

The loss every language model trains on, built from logits with no torch in sight. The screen for whether you understand log-softmax and the numerical-stability trick, not just import nn.CrossEntropyLoss.

BPE is how GPT and most LLMs turn text into tokens, and building a tiny trainable version from a corpus is a favorite deep-dive. The screen for whether you understand the merge loop, deterministic tie-breaking, and round-trip decode, not just tiktoken.encode.

The most common screen opener, and interviewers use it to check whether hashmap thinking is reflexive. Here's the one-pass answer, the narration that earns points, and the duplicate-handling edge case most candidates fumble.

A 5-minute warm-up that quietly tests the most useful idea in practical coding: choosing a canonical key. The sorted-string vs character-count tradeoff is exactly what interviewers want to hear you reason about.

The interval pattern shows up everywhere in FDE loops: calendars, flight segments, log windows. Master the sort-then-sweep idiom here and three other interview questions fall out for free.

Top-K is the most reused primitive in FDE interviews: it reappears inside log parsers, analytics questions, and retrieval ranking. Three solutions exist; knowing which one to lead with is the real test.

The reported ElevenLabs live round, written in a shared Google Doc, no execution, no autocomplete. Inherited folder permissions plus permission-change timestamps, and the two-axis resolution rule that keeps the code to thirty lines.

The canonical grid question, and the template for every flood-fill, shortest-path, and region-labeling variant FDE loops throw at you. BFS vs DFS vs union-find: here's which to write and which to merely mention.

The quintessential FDE practical: messy real-world input, grouping, ranking, and a hidden rubric line for how you treat malformed data. Most candidates parse happily and lose the round on the lines that don't parse.

The gentlest 'design a class' question in FDE screens, and the warm-up interviewers use before rate limiters and sessionization. The deque trick is easy; the API and time-window follow-ups are where the signal is.

The single most-asked design-a-data-structure question in FDE loops. There's an interview-legal Python shortcut and a from-scratch version, and knowing when to offer which is half the grade.

A verbatim-reported multi-part at OpenAI and Anthropic. Each level breaks the previous design on purpose: the burst-at-the-boundary flaw is planted, and naming it before the interviewer does is how you win the round.

The follow-up interviewers reach for when merge-intervals goes too smoothly. The three-phase scan is elegant, but only if you've internalized the overlap condition most candidates have to re-derive under pressure.

Looks like a sliding-window problem; isn't one. The prefix-sum + hashmap trick that solves it is the same idea behind sessionization and cumulative-metrics questions later in the loop: learn it once, reuse it three times.

Sessionization is the analytics primitive behind every 'how do users actually behave' question, and a favorite practical screen. The algorithm is easy; out-of-order events and duplicates are where the round is actually scored.

The reported Anthropic concurrency round: the crawler is twenty minutes, the follow-ups are the interview. Why threads work despite the GIL, what asyncio buys you, and where the semaphore goes. The answers, with the race condition graders plant.

Everyone knows the sort + two-pointer outline. The interview is actually about duplicate handling: three separate skip conditions that candidates routinely botch live. Here's the clean version and the narration that goes with it.