Zendesk AI & ML Engineer interview questions

Every team ships English first, then finds that the retriever, the prompt, the latency budget and the cost model each degrade differently in the next five languages. The applied playbook looks nothing like the model-level answer, and the thing that sinks launches is the eval set nobody built.

An AI support agent handling millions of chats will sometimes be wrong, rude, or unhelpful, and you cannot read them all. Monitoring quality at that scale is its own design problem with its own failure modes.

The modal Applied AI design round. Anyone can draw embed-retrieve-generate. The signal is in chunking, hybrid retrieval, the rerank/latency tradeoff, and how you prove it works. Here is the structure that scores.

A favorite practical screen at the labs, because it tests concurrency, time handling, and judgment in 20 lines. The trap is the background thread that wastes CPU. Here is the lazy-refill version interviewers want, plus the follow-ups.

Agents are over-applied. The strong answer resists the hype: most tasks want a single structured call, and agents earn their cost only under specific conditions. Here is when to reach for one and how to stop it from compounding errors.

The two knobs that make or break a RAG system, and the ones candidates hand-wave. The signal is tuning chunking against recall and knowing exactly what dense retrieval misses that BM25 catches. Here is the reasoning, not the rules of thumb.

A classic that tests whether you reach past sorting for the right structure. The signal is the heap solution (O(n log k)), the bucket-sort O(n) trick, and how it changes for an unbounded stream. Here is the answer.

A sudden 22-point AUC jump is a gift and a warning. Junior candidates celebrate; strong ones get suspicious and know exactly which leakage checks to run before anything ships.

Pipelines fail and retry; the real question is whether a retry corrupts your data. The signal is idempotent writes (not 'exactly-once delivery') plus watermarks for late data. Here is how to make re-runs safe without double-counting.

Most RAG systems ship with no real eval, which is why most RAG systems quietly degrade. The signal is decomposing evaluation into retrieval and generation, and measuring faithfulness separately from relevance. Here is the framework that makes RAG improvements measurable.

Enterprise RAG fails on isolation, not retrieval quality. The signal is enforcing tenant and document-level permissions server-side, at retrieval time, so the model can never surface data a user cannot see. Here is the design that survives a security review.

Autocomplete is the canonical trie question, and it tests whether you reach for the right structure instead of scanning a word list. The signal is O(prefix) lookup, the DFS to collect completions, and the follow-ups (ranking, memory). Here is the build.

Sliding window is one of the highest-yield coding patterns, and this is its canonical problem. The real signal is recognizing when a window collapses an O(n squared) scan into one linear pass, and maintaining the invariant cleanly.

Graph traversal underpins a huge class of interview problems. The signal is knowing the BFS-vs-DFS tradeoff (shortest path vs memory shape) and applying it cleanly, then handling the directed-vs-undirected cycle gotcha most candidates miss.

A common ML-coding check that verifies you actually understand the metrics you quote. The signal is getting TP/FP/FN/TN right and knowing macro vs micro averaging for multiclass, not reaching for sklearn.

Bit-manipulation problems screen for comfort with binary and the O(1)-space tricks most candidates fumble. The tell is whether you reach for XOR's self-canceling property and the n & (n-1) idiom on instinct. Here is the toolkit.

K-th largest has three textbook solutions, and the signal is knowing Quickselect's average O(n) beats sorting's O(n log n), why its worst case is O(n²), and when a heap is actually the better call. Here is the answer.

The DP that exposes why greedy quietly breaks on non-standard denominations, and how one bottom-up table fixes it. The signal interviewers want is the dp[amount] recurrence and the unbounded-reuse insight.