HeyJaredAI Interview Prep
AI Interview Prep delivers in-depth insights into advanced NLP, CV, RL, LLMs, ML System Design. We highlight common traps and proven strategies to help engineers excel in technical interviews.
- Indexed issues, last 90 days
- 26
- Latest publication
- Sep 30, 2026
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- Earliest in this view
- Sep 2, 2026
Latest issues
AI Agent Engineering Interview #11 - The Near-Duplicate Leakage Trap (opens the original)
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You’re in a Senior ML Engineer interview at Google DeepMind and the interviewer asks: “You scraped GitHub, held out a random 5% of files as your test set, and your new code model just posted a big jump on it. Before you announce anything, what’s the first thing you’d suspect about how that split was built?”Don’t say: “Overfitting. I’d check the training curves and add regularization.”Wrong diagnosis. The model didn’t overfit your training set. Your test set is part of your training set.<p
AI Agent Engineering Interview #10 - The Prompt Boundary Tokenizer Trap (opens the original)
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You’re in a Senior AI Engineer interview at Microsoft, and the interviewer asks:“A teammate retrained our code tokenizer to merge freely across whitespace. Sequences got ~40% shorter and held-out loss improved. Why might IDE autocomplete get worse after we ship it, and what do you check before approving?”Don’t say: “Shorter sequences plus lower loss means a better model. Ship it.”That answer misses the problem entirely.The reality: held-out loss is measured on complete files. Autocomplete runs o
AI Agent Engineering Interview #9 - The Role-Based Multi-Agent Trap (opens the original)
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You’re in a Senior AI Engineer interview at Google DeepMind and the interviewer asks:“Your ‘AI software team’ of PM agent → coder agent → tester agent beats a single agent on standard GitHub issues. But the moment a task doesn’t fit that shape, it falls apart. Why? And when does one well-managed agent beat the whole team?”Don’t say: “Specialized agents are better at their roles. The single agent just gets overloaded with context.”That’s backwards, and it’s exactly why the sy
AI Agent Engineering Interview #8 - The Skill Evaluation Trap (opens the original)
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You’re in a Senior AI Engineer interview at Google and the interviewer asks:“Your team shipped a code-review skill for your coding agent six months ago. Nobody knows if it helps. How do you build a production feedback loop that measures its quality from real PRs and turns that evidence into concrete skill edits?”Don’t say: “I’d have an LLM judge score the reviews, then ask the model to improve the prompt.”Scoring reviews on their own tells you how good a review sounds, not whether it changed any
AI Agent Engineering Interview #7 - The A Priori Skill Trap (opens the original)
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You’re in a Senior AI Engineer interview at OpenAI and the interviewer asks:“Your PM wants to save engineering time: ‘Just have the agent write its own skills from the task description.’ Benchmarks show this can make the agent worse. Why? And what should a skill actually be built from?”Don’t say: “The model isn’t smart enough yet. Use a stronger model and a better prompt.”Too shallow. A stronger model working from the same thin input still produces a thin skill.The reality: a
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