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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. 1Algorithms 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 FoundationsEasy12 min
  2. 2Softmax, 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
  3. 3PyTorch Training LoopsBuild a PyTorch classifier from raw logits through autograd, validation, and reloadable checkpoints.ML Algorithms & EvaluationMedium17 min
  4. 4LLM 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 FoundationsMedium20 min
  5. 5Evaluating 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
  6. 6LLM-as-a-Judge EvaluationAdd calibrated soft judgments to a RAG evaluation trace without letting an LLM override deterministic evidence gates.Applied LLM EngineeringMedium18 min
  7. 7Code Completion SystemDesign a real-time code completion path with context construction, measured serving latency, privacy controls, and stale-result suppression.System Design CapstonesHard42 min
  8. 8Multi-Tenant LLM PlatformDesign a shared LLM platform with tenant-scoped state, quota enforcement, adapter routing, KV accounting, and measured GPU utilization.System Design CapstonesHard35 min
  9. 9Reasoning & 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 CapstonesHard43 min
  10. 10AI 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 InterviewingHard26 min
  11. 11AI 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 InterviewingHard24 min
  12. 12AI Lab Technical PresentationPrepare a technical project presentation that proves ownership, architecture taste, tradeoff judgment, rollout discipline, metrics, and depth under questioning.AI Lab InterviewingHard21 min