xAI Forward Deployed Engineer interview questions

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.

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.

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 follow-up to number-of-islands, and the question where candidates reveal whether they understand WHY BFS finds shortest paths. Plus the two variants (weights, eliminating obstacles) that decide hard-screen outcomes at xAI.

The third member of the grid trilogy, and the one that tests backtracking discipline: marking, unmarking, and the early-exit pruning that turns a timeout into a pass.

Exact-match pub/sub is a dict of lists. The round is decided at level two (wildcard matching on hierarchical topics) and at level three, when the interviewer asks what happens when a subscriber's callback throws.

Kahn's algorithm gets you level one. The round is won at level three, where 'run up to K tasks at once' breaks naive topo sort, and the indegree bookkeeping you chose at L1 either saves you or sinks you.

The OpenAI FDE signature question, reported near-verbatim for two years: four levels in ~60 minutes, where L4 silently breaks every naive L3 implementation. The lazy-expiry design that survives all four levels, with the pacing plan.

AI-native companies ask this to separate engineers who move fast with judgment from those who just move fast. The scored part isn't the speed, it's the corner-selection logic. Here's the framework.

The xAI signature round, 30 minutes of 'why?' aimed at one project, designed to expose orchestrators posing as builders. Here's how to choose the project and survive the drill.

Kernel optimization is mostly the art of feeding 100+ TFLOPs from the small fast end of this hierarchy. Know the rough latencies and bandwidths, interviewers listen for the numbers.

A staple NVIDIA screen with a precise mechanical answer, and a follow-up trap about which branches actually diverge that catches most candidates.

The two access-pattern killers behind most 'my kernel is 10x slower than cuBLAS' mysteries. The matrix transpose walks straight into both, which is why NVIDIA loves asking about it.

A real infra-loop question that candidates fumble by guessing. The correct map, TP on NVLink, PP and DP across the scale-out fabric, falls out of three traffic profiles, and the reasoning is the answer.

NCCL's topology tricks, GPUDirect RDMA, and the single most misleading metric in distributed training. If you've ever trusted nvidia-smi on a slow run, this question was written for you.

Both carry RDMA at 400Gb/s; the decision is about loss behavior, tuning burden, and who operates it. A defensible recommendation with the conditions that flip it, the format this question is scored on.

The MoE question frontier labs use to test whether you see routing as a systems problem, not just a modeling trick. Covers the collapse spiral, auxiliary losses, capacity factors, and what changes at serving time.

At 16k GPUs something fails every few hours, Meta logged 466 interruptions in 54 days training Llama 3. The checkpoint-interval math, straggler detection, and automation that turn failures into a budget line instead of an emergency.

The resume-walk question for infra roles, with a hidden rubric: layer ownership, scale fluency, and one defensible war story. How to answer credibly at any actual scale, and the inflation tells interviewers catch.

A reported OpenAI design prompt. The skeleton: gang scheduling, topology-aware placement, preemption with checkpoint contracts, and quota, plus the fragmentation problem that quietly eats 20% of naive clusters.

The worst failure mode in distributed training: no crash, no log, every GPU pinned at 100%, and the job dead. The triage order that finds a collective mismatch or a dead rank before you burn a day of cluster time.

One synchronous job, 16k GPUs, and any single component can stall all of them. The blast-radius analysis interviewers want: which faults take down a rack vs the run, where the shared single points of failure hide, and how parallelism layout maps onto failure domains.

No error, no hang, the loss still moves, but the run is suddenly half as fast and burning the same dollars. The triage that separates a straggler from a fabric problem from broken comm/compute overlap, using the signals nvidia-smi can't give you.

The warm-up that quietly filters out candidates who only know LLMs as a magic API. Nail the two-phase inference picture and you set up every later answer about latency, cost, and hallucinations.

Everyone can define it; few candidates know why 'advertised context' and 'usable context' aren't the same thing. The cost, latency, and attention caveats that make this answer stand out.

Modern LLMs share one architectural skeleton. Learn the five components and you can read almost any model card cold, plus the single distinction that proves to an interviewer you actually understand it rather than the buzzwords.

Tokens drive your customer's bill, their latency, and the weird failures like botched arithmetic. The rough numbers every FDE should rattle off, plus the trap answers interviewers listen for.

Every FDE will face an angry customer asking this. The mechanistic explanation interviewers want (why hallucination is the default behavior, not a bug) plus the framing that defuses customer escalations.

Every queue, webhook, and retry loop you'll ever deploy at a customer hides this distinction. Interviewers use it to separate people who've shipped from people who've read about shipping.

A deceptively simple sampling question with one trap (does temperature 0 fix hallucinations?) and one senior move interviewers reward. Know which workflows want zero.

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.