SpaceX AI & ML Engineer interview questions

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.

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.

Applied AI rarely waits for certainty, and companies (Amazon's Bias for Action, NVIDIA's speed) test whether you can act decisively without freezing or being reckless. The signal is a reversible, well-reasoned call with a plan to validate. Here is the arc that lands.

The canonical grid DP where each cell depends on three neighbors at once. The signal interviewers want is the min-of-three recurrence, and the reason it has to be min. Here is the answer.

Moderation is a multi-stage classification problem with brutal tradeoffs: false negatives cause real harm, false positives censor legitimate users, and the adversary adapts. The signal is the tiered pipeline, per-severity precision/recall calibration, and human-in-the-loop. Here is the design.

Applied AI is half translation. Interviewers ask this to see whether you can drop the jargon, frame tradeoffs in business terms, and earn trust with people who control the roadmap. Here is how to show that skill in the answer itself.

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.

House Robber is the cleanest choose-with-a-constraint 1D DP, and it collapses to O(1) space. The signal is the take-this-or-skip recurrence and the two-variable rolling optimization.

Sorting an array of three values in one pass tests the three-pointer partition (Dutch National Flag). The signal is the low/mid/high invariant and knowing when not to advance mid. Here is the answer.

Spiral traversal tests careful boundary management more than algorithmic insight. The signal is shrinking four boundaries layer by layer without double-visiting a row or column. Here is the answer.