Every issue of AI Interview Prep: the question, where it was set, and the answer most candidates give. Search by concept, or filter by series and company. The full answers live on Substack.
326 traps
Staff AI Engineer interview at Anthropic
“Product wants one model that flips between instant answers and long reasoning with a prompt tag, so we only run one deployment. Reasoning benchmarks dropped 2 points. Do you ship it?”
LLM System Design Interview #75
Sep 19, 2026
Senior AI Engineer interview at OpenAI
“Your RLVR math pipeline has a verifier, so reward hacking isn’t your problem. But accuracy has plateaued far below what manual review says the model can do. Where is ‘verifiable’ failing you?”
LLM System Design Interview #74
Sep 18, 2026
Senior ML Engineer interview at Google DeepMind
“You switched to GRPO. Average chain-of-thought length grows every training step. Leadership calls it ‘the model learning to think harder.’ What’s the less flattering explanation, and how do you verify it before your inference bill doubles?”
LLM System Design Interview #73
Sep 17, 2026
Senior ML Engineer interview at OpenAI
“Your PPO reward model scores climbed for six weeks while you kept adding compute. Human evals got worse. Why did more RL compute stop working, and what does your reward need before scaling RL actually pays off?”
LLM System Design Interview #72
Sep 16, 2026
Senior LLM Post-Training Engineer interview at OpenAI
“Your new SFT mix lifts head-to-head win rates by 15 points, but MMLU, GSM8K, and your internal capability suite are completely flat. Leadership wants to ship it as a capability gain. What do you tell them?”
LLM System Design Interview #71
Sep 15, 2026
Staff AI Engineer interview at OpenAI
“Your team paid expert annotators to write the highest-quality SFT responses possible, detailed, sourced, with citations. After fine-tuning, factual hallucination rate went up. What happened?”
LLM System Design Interview #70
Sep 14, 2026
Senior Data Engineer interview at Anthropic
“You swapped Common Crawl’s WET files for your own extraction off the raw WARC files. Same URL list, same filters, same model, but your benchmark scores moved. Your teammate says extraction is just plumbing. What do you tell him?”
LLM System Design Interview #69
Sep 13, 2026
Senior Data Infrastructure Engineer interview at Anthropic
“Your crawler is hitting record pages-per-second. A week later a site owner is publicly complaining you hammered their servers a million times in 24 hours, and your corpus is 40% near-duplicates. Which crawl-policy decisions did you get wrong?”
LLM System Design Interview #68
Sep 12, 2026
Senior AI Engineer interview at Google DeepMind
“Your VP says: ‘We scraped the web in 2020 and got a great corpus. Just re-run the crawler and we’ll get the same data, only bigger.’ Why is that wrong, and how does it change your pre-training data strategy?”
LLM System Design Interview #67
Sep 11, 2026
Staff Research Engineer interview at Anthropic
“Your team wants to swap AdamW for Muon on the next frontier run because it wins on small-scale benchmarks. What two axes must their ablation cover before you sign off?”
LLM System Design Interview #66
Sep 10, 2026
Principal ML Engineer interview at Anthropic
“StepFun says optimal batch size depends only on total tokens D, and optimal LR goes up with more data. DeepSeek says LR just decreases with compute. Both trained real frontier models. Which one do you use for our next run?”
LLM System Design Interview #65
Sep 9, 2026
Staff ML Engineer interview at Anthropic
“You ran one big grid search over learning rate and batch size. The batch-size scaling law is a gorgeous straight line in log-log. The learning-rate fit is a shotgun blast with a trendline drawn through it out of politeness. Same runs, same data. Why?”
LLM System Design Interview #64
Sep 8, 2026
Senior ML Engineer interview at Anthropic
“You switched your scaling law sweeps from cosine to WSD (warmup-stable-decay). Two weeks in, the intermediate loss curves look strictly worse than the cosine baselines and leadership wants to roll back. What do you tell them?”
LLM System Design Interview #63
Sep 7, 2026
Senior AI Research Engineer interview at Anthropic
“Your team burned three weeks sweeping learning rates at every rung of the scaling ladder before the 7B run. A colleague says muP would have let you tune once at 100M and transfer for free. Were they right?”
LLM System Design Interview #62
Sep 6, 2026
Senior ML Engineer interview at OpenAI
“Your model hits 40% MFU in training. In production, prefill looks healthy but the GPUs sit at 4% utilization during generation. Nothing is broken. What’s structurally different, and why won’t bigger batches fix it?”
LLM System Design Interview #61
Sep 5, 2026
Principal ML Infrastructure Engineer interview at NVIDIA
“Your pipeline-parallel run is showing 60% GPU idle time. Your junior says ‘just increase the micro-batches.’ Is he right?”
LLM System Design Interview #60
Sep 4, 2026
Senior ML Infrastructure Engineer interview at NVIDIA
“Your DDP run is healthy on 8 GPUs. You scale to 64 across 8 nodes and per-GPU throughput drops 40%. Why and what should you have calculated before you bought the nodes?”
LLM System Design Interview #59
Sep 3, 2026
Senior ML Systems Engineer interview at Meta
“You moved your matmuls to FP8 on H100s expecting a 2× speedup. You got 25%. Is FP8 overhyped, or did you do it wrong?”
LLM System Design Interview #58
Sep 2, 2026
Staff ML Engineer interview at Anthropic
“Your model is 80 layers deep and your infra team is furious. Why does almost every production LLM land near a 100:1 width-to-depth ratio?”
LLM System Design Interview #57
Sep 1, 2026
Senior LLM Research Engineer interview at Meta
“You swapped your ReLU FFN for SwiGLU, loss dropped 2%, and you shipped it. Why should I reject that result?”
LLM System Design Interview #56
Aug 31, 2026
Senior LLM Training Engineer interview at Anthropic
“Your profiler says LayerNorm is 0.17% of your FLOPs but 25% of your step time. Do you optimize it?”
LLM System Design Interview #55
Aug 30, 2026
Senior LLM Training Engineer interview at Anthropic
“You moved LayerNorm out of the residual stream and your gradient spikes disappeared. Your colleague says you should have used post-norm-outside-residual like Grok, Gemma 2, and OLMo 2. Who’s right, and what does ‘keep the residual stream clean’ actually buy you at the gradient level?”
LLM System Design Interview #54
Aug 29, 2026
Senior ML Engineer interview at OpenAI
“Your tokenizer fails to round-trip on 0.3% of production traffic, emoji, mixed-script usernames, truncated UTF-8 from a bad client. Your teammate maps them all to UNK and ships. What did he just break?”
LLM System Design Interview #53
Aug 28, 2026
Senior ML Engineer interview at Anthropic
“Your BPE encode() is correct and passes every round-trip test. But tokenizing your 2TB pretraining corpus would take three weeks. Your tech lead says rewrite it in Rust. Why is that the second thing you should do?”
LLM System Design Interview #52
Aug 27, 2026