Google DeepMind AI & ML Engineer interview questions

A fixed 1000-token budget forces the search choice. Step-level beam search guided by a process-reward model beats best-of-N and MCTS on accuracy per token for math, and you collect the PRM labels automatically with Monte-Carlo rollouts, no human step annotation.

The thing interviewers actually screen for isn't the Bellman equation, it's whether you know when NOT to use RL. Most problems that look like RL are better solved by supervised learning or a bandit, and reaching for full RL when you have labels is a classic over-engineering tell.

Turn a constitution of rules into a model that follows them, with no human labels on harmful examples. The strong answer walks the two phases (self-critique SFT, then RL from AI feedback), then spends real time on the part papers gloss: adversarially validating the aligned model holds under attack.

A code model that occasionally produces ransomware, reverse shells, or exfiltration scripts. The strong answer builds an automated loop: adversarial prompt generation, static and behavioral output scanning, layered mitigations, and attack-success-rate per category to prove the mitigations worked.

SAEs can surface candidate 'deception' features in the residual stream, but a correlated feature is not a cause. The real work, and the honest answer, is causal validation and admitting what interpretability cannot yet prove.

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.

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 staple at OpenAI and Anthropic in both coding and design rounds. Most candidates can name the algorithms; few can say which one lets 2x your limit through, and that's the part that gets scored.

Averages lie, and interviewers know most candidates quote them anyway. The fastest way to sound production-grade in any latency discussion is to reason about percentiles correctly, including the one trap almost everyone falls into.

The signature FDE debugging genre: a moving fault, an opaque client, and a customer who's sure it's your fault. The winning method splits the problem at the boundary, and knows the four classic culprits hiding on their side.

The NVIDIA warm-up that exposes whether you've written a kernel or just called .cuda(). The mapping from software hierarchy to silicon explains every GPU performance conversation that follows.

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.

A Fermi estimate with a paycheck attached. The interviewers' favorite filter: candidates who jump to a dollar figure miss the two numbers that actually break deployments, peak QPS and tokens-per-minute limits.

One malformed message can silently wedge an entire pipeline, and it happens at every customer eventually. The DLQ answer interviewers want covers what most candidates forget: what happens *after* the message lands there.

Every FDE loop opens with this, but the definitions are table stakes. What gets scored is whether you can map each metric to a dollar cost the customer recognizes, here's the framing that does it.

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.

This isn't a deployment question, it's a judgment question wearing one. Interviewers are scoring whether you have a decision framework, a safety checklist, and the spine to say no to a customer politely.

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.

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.

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.

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.