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AI Lab Interview Prep

Practice building, debugging, and explaining AI systems so you have concrete work and clear reasoning to bring into an interview.

Engineers preparing for AI lab, applied research engineer, or LLM systems interviews.

You can practice the concepts interviewers probe while still learning the underlying systems deeply.

  1. 1Python for AI EngineeringRebuild the Docker lesson's 2/3 receipt in Python, and keep a bad JSONL field from looking like a wrong answer.Computing FoundationsEasy20 min
  2. 2Data Structures for AIThree notes can be scanned with a list. The same search gets a different cost once each operation picks a list, index, heap, queue, or cache.Computing FoundationsEasy24 min
  3. 3Algorithms for ML EngineersWhen four authorized chunks become 50,000, count which operation repeats before you choose a scan, heap, prefix tree, or latency guard.Computing FoundationsEasy29 min
  4. 4Probability for Machine LearningA detector that catches 95 percent of abusive signups can still fill the review queue with clean accounts, and the flagged-pile counts show why.Math & StatisticsEasy23 min
  5. 5Hypothesis Tests, Intervals, and pass@kJudge one extra coding pass with a paired interval, and read pass@k only under a matched attempt budget.Math & StatisticsEasy28 min
  6. 6Softmax, Cross-Entropy & OptimizationTurn raw class scores into stable probabilities and a useful learning signal, then apply the same loss to next-token predictions.Preparation & PrerequisitesEasy27 min
  7. 7PyTorch Training LoopsA falling loss can hide a broken ticket-router loop, so follow one batch through logits, gradients, validation, and a checkpoint that still matches after reload.ML Algorithms & EvaluationMedium36 min
  8. 8AI Lab Coding Interview: Python SystemsWhen the next requirement arrives, keep the claim, deadline, or request key that already made the first test pass.AI Lab InterviewingHard34 min
  9. 9The Transformer Architecture End-to-EndFollow `function returns the` through a causal decoder until the last row becomes a score for the next token.Preparation & PrerequisitesEasy26 min
  10. 10LLM Benchmarks & LimitationsA fluent CodeAssist answer invents on-call deploy permission the freeze policy never granted. A public score will not catch that.Core LLM FoundationsMedium35 min
  11. 11Experiment Design and A/B TestingRandomize incidents for a query rewrite, then hold the launch unless the lift and the guardrails both hold.ML Algorithms & EvaluationMedium43 min
  12. 12Evaluating AI AgentsAn agent says it is done. Learn to check what changed, whether it respected permissions, and how reliably it can repeat the task.Applied LLM EngineeringMedium33 min
  13. 13LLM-as-a-Judge EvaluationTwo rollback replies both follow DEP-27, but a string match can't say which one an on-call engineer can use.Applied LLM EngineeringMedium26 min
  14. 14Inference: TTFT, TPS & KV CacheA blank chat window and a busy GPU can both be right: prefill and decode hit different limits, and the KV cache decides how many sequences fit.Inference & Production ScaleHard47 min
  15. 15Code Completion SystemA suggestion for `is_allowed =` can arrive after the developer has typed past it. Keep the useful ones, and don't paint the stale line.System Design CapstonesHard60 min
  16. 16Multi-Tenant LLM PlatformA long design review and a short incident summary share GPUs, but neither should wait unfairly or see the other's evidence. Design the identity, quota, scheduling, and cache controls.System Design CapstonesHard35 min
  17. 17Reasoning Agent System DesignTests pass, but canary latency spikes after a config change. Design an agent that gathers the evidence, spends extra reasoning only when needed, and recommends whether to hold, roll back, or continue.System Design CapstonesHard52 min
  18. 18AI Lab System Design InterviewWhen a queue will miss the first-token SLO, the design still has to name the client response, the owner, and the support receipt.AI Lab InterviewingHard43 min
  19. 19AI Lab Behavioral InterviewA polished answer says you balanced safety and speed. A credible one names the failing cases and why traffic stayed at zero.AI Lab InterviewingHard30 min
  20. 20AI Lab Technical PresentationYou have fifteen minutes. Make one decision, one failure, and one number defensible, with the number's scope and limits clear.AI Lab InterviewingHard18 min