Skip to content
LeetLLM
Tracks
Advanced20 lessons

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 by building a small evaluator: read JSONL, validate fields, count correct predictions, and test the difference between bad input and a wrong answer.Computing FoundationsEasy17 min
  2. 2Data Structures for AIChoose Python lists, hash-based collections, deques, heaps, trees, graphs, tries, and caches by the operations an AI system must serve.Computing FoundationsEasy21 min
  3. 3Algorithms for ML EngineersCount work in a small retriever, compare scans, heaps, and radix trees, evaluate prefill versus decode complexity, and enforce latency budgets in production AI pipelines.Computing FoundationsEasy27 min
  4. 4Probability for Machine LearningUse one API abuse-risk detector to learn events, random variables, distributions, conditional probability, independence, Bayes rule, and base-rate mistakes.Math & StatisticsEasy21 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 & StatisticsEasy26 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 & PrerequisitesEasy26 min
  7. 7PyTorch Training LoopsTrace logits, gradients, updates, and validation in runnable PyTorch loops. Work through unequal micro-batches, CUDA AMP ordering, independent snapshots, and checkpoint resumption.ML Algorithms & EvaluationMedium34 min
  8. 8AI Lab Coding Interview: Python SystemsBuild production-shaped Python systems under staged requirements: crawlers, TTL stores, schedulers, token buckets, ledgers, and thread-safe claim points.AI Lab InterviewingHard34 min
  9. 9The Transformer Architecture End-to-EndTrace a three-token prompt through causal attention, residual updates, and a vocabulary head. Build and test one complete decoder in PyTorch.Preparation & PrerequisitesEasy25 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 FoundationsMedium33 min
  11. 11Experiment Design and A/B TestingDesign a trustworthy online experiment for an incident-assistant change: randomize incidents, measure useful outcomes, quantify uncertainty, and reject false wins.ML Algorithms & EvaluationMedium41 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 EngineeringMedium31 min
  13. 13LLM-as-a-Judge EvaluationBuild rubric-based judges, detect unstable preferences, and measure agreement without mistaking a plausible score for ground truth.Applied LLM EngineeringMedium25 min
  14. 14Inference: TTFT, TPS & KV CacheMap prefill vs decode bottlenecks, measure TTFT and decode cadence, and size KV cache so concurrent sequences fit on one GPU.Inference & Production ScaleHard42 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 CapstonesHard49 min
  16. 16Multi-Tenant LLM PlatformDesign shared LLM serving with trusted tenant identity, atomic reservations, fair scheduling, scoped caches, and reconcilable usage records.System Design CapstonesHard28 min
  17. 17Reasoning Agent System DesignDesign 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 CapstonesHard51 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 InterviewingHard42 min
  19. 19AI Lab Behavioral InterviewTurn AI lab values into inspectable engineering stories: launch gates, incidents, disagreement, and ownership with metrics, not slogans.AI Lab InterviewingHard29 min
  20. 20AI Lab Technical PresentationBuild a role-appropriate technical talk with explicit ownership, defensible comparisons, concrete failure cases, and honest limits on what the evidence proves.AI Lab InterviewingHard18 min