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AI Lab Interview Prep

A focused path for frontier AI interviews: implementation speed, system design, eval judgment, and technical communication.

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 EngineeringLearn Python as the first AI engineering loop: read JSONL rows, validate fields, compute exact-match accuracy, and harden the scorer with pytest, prompt snapshots, leakage checks, seeded runs, and a CI gate.Computing FoundationsEasy14 min
  2. 2Data Structures for AIChoose lists, inverted indexes, heaps, queues, and caches by the operations an AI system must serve.Computing FoundationsEasy12 min
  3. 3Algorithms for ML EngineersLearn to count retrieval work, express growth with Big-O, avoid wasteful selection and pairwise loops, and enforce a latency budget with runnable Python.Computing FoundationsEasy11 min
  4. 4Probability for Machine LearningUse one API abuse-risk detector to learn events, priors, conditional probability, independence, Bayes rule, and base-rate mistakes.Math & StatisticsEasy15 min
  5. 5Hypothesis Tests, Intervals, and pass@kCompare a code-generation model with paired evidence, uncertainty for lift, and pass@k under a fixed sampling budget.Math & StatisticsEasy13 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 & PrerequisitesEasy13 min
  7. 7PyTorch Training LoopsBuild a PyTorch classifier from raw logits through autograd, validation, and reloadable checkpoints.ML Algorithms & EvaluationMedium17 min
  8. 8AI Lab Coding Interview: Python SystemsPractice production-shaped Python coding prompts: crawlers, in-memory stores, ledgers, schedulers, parsers, rate limiters, caches, and concurrency follow-ups.AI Lab InterviewingHard25 min
  9. 9The Transformer Architecture End-to-EndTrace an incident note through masked attention, a decoder block, and next-token logits with readable NumPy and PyTorch code.Preparation & PrerequisitesEasy13 min
  10. 10LLM Benchmarks & LimitationsBuild an evaluation suite for a policy-answering LLM: score evidence use, understand public benchmark contracts, control judge bias, and make release decisions from private tests.Core LLM FoundationsMedium21 min
  11. 11Experiment Design and A/B TestingDesign a trustworthy online experiment for an AI support change: randomize customers, measure useful outcomes, quantify uncertainty, and reject false wins.ML Algorithms & EvaluationMedium20 min
  12. 12Evaluating AI AgentsEvaluate model-promotion agent runs by final state, observable trace, safety gates, cost, and repeatability, then map private tests to public benchmarks.Applied LLM EngineeringMedium18 min
  13. 13LLM-as-a-Judge EvaluationAdd calibrated soft judgments to a RAG evaluation trace without letting an LLM override deterministic evidence gates.Applied LLM EngineeringMedium18 min
  14. 14Inference: TTFT, TPS & KV CacheUnderstand the two-phase inference process (prefill vs decode), derive the KV cache memory formula, and learn production optimizations like chunked prefill and prefill/decode disaggregation.Inference & Production ScaleHard29 min
  15. 15Code Completion SystemDesign a real-time code completion path with context construction, measured serving latency, privacy controls, and stale-result suppression.System Design CapstonesHard42 min
  16. 16Multi-Tenant LLM PlatformDesign a shared LLM platform with tenant-scoped state, quota enforcement, adapter routing, KV accounting, and measured GPU utilization.System Design CapstonesHard34 min
  17. 17Reasoning & Test-Time ComputeDesign a production reasoning agent that routes by difficulty, evaluates candidate work, requires evidence before release, and survives serving bottlenecks like key-value (KV) cache growth.System Design CapstonesHard45 min
  18. 18AI Lab System Design InterviewDesign AI lab systems with clear goals, scale math, APIs, data models, overload behavior, permissions, eval gates, and operational debugging paths.AI Lab InterviewingHard26 min
  19. 19AI Lab Behavioral InterviewPrepare behavioral answers for AI labs around judgment, humility, incident leadership, disagreement, safety mechanisms, ambiguity, and evidence of ownership.AI Lab InterviewingHard17 min
  20. 20AI Lab Technical PresentationPrepare a technical project presentation that proves ownership, architecture taste, tradeoff judgment, rollout discipline, metrics, and depth under questioning.AI Lab InterviewingHard15 min