The complete prep resource for AI Engineer, LLM Engineer, and Applied AI interviews.
1,067 questions with worked answers, 10 system design case studies, 19 runnable coding challenges, company interview questions for 33 companies, and guides for 10 engineering roles.
Company interview questions:
Anthropic ·
OpenAI ·
Google DeepMind ·
Meta ·
xAI ·
Mistral ·
DeepSeek ·
Moonshot AI ·
Zhipu AI ·
Sarvam AI ·
Microsoft ·
Amazon ·
Apple ·
NVIDIA ·
Qwen (Alibaba) ·
Databricks ·
Scale AI ·
Perplexity ·
Cursor ·
Cohere ·
Hugging Face ·
Together AI ·
Glean ·
Palantir ·
Sierra ·
Harvey ·
Abridge ·
Cognition ·
Groq ·
ElevenLabs ·
Character.AI ·
Waymo ·
Figure AI
The AI Engineer 75 is this repo's Blind 75: the 75 highest-signal items across everything here, in checkbox form.
| What | How many | Bar to clear |
|---|---|---|
| Must-know questions | 64 | Answer out loud, cold, before opening the answer |
| System design mocks | 4 | 45-60 minutes each on a doc before reading the solution |
| From-scratch coding challenges | 7 | Working implementation before peeking at the reference |
If you can clear all 75, you are ready for most AI engineering loops. If you can't, the gaps tell you exactly which topic to study. Everything else in this repo is the depth behind this list.
| Your runway | Do this | Volume |
|---|---|---|
| Days | The AI Engineer 75, then CHEATSHEET.md the night before | 75 items + one evening review |
| 1 week | The 75 + the 1-week cram plan | 75 items + 6 crash courses |
| 2-4 weeks | The 4-week plan: all 13 crash courses, full question banks on your weak topics, 3 design mocks | 200+ questions + 3 case studies + all 19 challenges |
| 1-2 months+ | The 8-week plan: all 541 topic questions, all 10 case studies, all 19 challenges, plus a portfolio project | Everything, plus building |
| Targeting a company | Add that company's interview questions from the banner above | +10-12 tailored questions and the loop map |
| Not an "AI Engineer" title | Start from your role guide instead | Role-calibrated study map |
Essential shortcuts: The AI Engineer 75 · Glossary · Mock interview kit · Night-before cheat sheet · Study plans · Curated papers and courses · aidaddy.tech, the companion site for AI system design and interview prep
Interview questions, loop maps, and prep priorities for 33 companies, tiered from frontier labs to applied AI shops and spanning the US, Europe, China, and India, including inference-silicon, vertical AI, autonomy and robotics. Everything is built from public information (job postings, engineering blogs, technical reports, published interview reports), not leaked material, and each page has a "last reviewed" note and sources.
| Company | Tier |
|---|---|
| Anthropic | Frontier lab |
| OpenAI | Frontier lab |
| Google DeepMind | Frontier lab |
| Meta | Frontier lab |
| xAI | Frontier lab |
| Mistral AI | Frontier lab (Europe) |
| DeepSeek | Frontier lab (China) |
| Moonshot AI | Frontier lab (China) |
| Zhipu AI | Frontier lab (China) |
| Sarvam AI | Frontier lab (India) |
| Microsoft | Big tech |
| Amazon | Big tech |
| Apple | Big tech |
| NVIDIA | Big tech |
| Qwen (Alibaba) | Big tech (China) |
| Databricks | AI-native & infra |
| Scale AI | AI-native & infra |
| Perplexity | AI-native & infra |
| Cursor (Anysphere) | AI-native & infra |
| Cohere | AI-native & infra |
| Hugging Face | AI-native & infra |
| Together AI | AI-native & infra |
| Glean | AI-native & infra |
| Palantir | Applied / forward-deployed |
| Sierra | Applied / forward-deployed |
| Harvey | Applied / vertical AI (legal) |
| Abridge | Applied / vertical AI (health) |
| Waymo | Applied / autonomy |
| Figure AI | Applied / robotics |
| Cognition (Devin, Windsurf) | AI-native & infra |
| Groq | AI-native & infra |
| ElevenLabs | AI-native & infra |
| Character.AI | AI-native & infra |
AI questions now show up in backend, frontend, product, data, DevOps, QA, mobile, and security loops, and the depth expected varies a lot by role. Each guide maps how that role's interviews changed, what you are actually expected to know (and what you are not), and 12-14 role-specific questions.
| Role | Guide |
|---|---|
| Backend Engineer | Guide |
| Frontend Engineer | Guide |
| Product / Full-stack Engineer | Guide |
| Forward Deployed Engineer | Guide |
| Data Engineer | Guide |
| DevOps / Platform / MLOps Engineer | Guide |
| QA / SDET Engineer | Guide |
| Mobile Engineer | Guide |
| Security Engineer | Guide |
| "Am I an ML Engineer or AI Engineer?" | Guide |
Each topic has a crash-course primer (README.md) and a full question bank with worked, collapsible answers (questions.md). Work with the answer collapsed until you have tried it yourself.
| # | Topic | Questions | What it covers |
|---|---|---|---|
| 01 | ML & Deep Learning Foundations | 50 | Bias-variance, optimization, regularization, metrics, loss functions, the fundamentals a fine-tuning or evals answer is built on |
| 02 | LLM & Transformer Fundamentals | 60 | Attention, positional encodings, tokenization, scaling laws, MoE, decoding, KV cache, reasoning models |
| 03 | Prompt Engineering & Context Engineering | 45 | Few-shot design, chain-of-thought, structured outputs, prompt caching, context rot and compaction |
| 04 | RAG & Retrieval | 55 | Chunking, embeddings, hybrid search, reranking, agentic RAG, retrieval evaluation |
| 05 | Fine-tuning, RLHF & Alignment | 51 | SFT, LoRA/QLoRA, DPO/PPO/GRPO, distillation, GPU memory maths for training |
| 06 | Agents, Tool Use & MCP | 60 | Tool calling, MCP, planning patterns, multi-agent design, agent evaluation and security |
| 07 | Evals & Observability | 50 | LLM-as-judge, benchmark limits, RAG and agent evals, tracing, regression testing |
| 08 | Inference, Serving & Production LLM Systems | 51 | Prefill vs. decode, KV cache paging, quantization, speculative decoding, cost engineering |
| 09 | Safety, Security & Responsible AI | 47 | Prompt injection, OWASP LLM Top 10, guardrails, agent security, data governance |
| 10 | Multimodal Models | 35 | Vision-language architecture, diffusion, ASR/TTS, voice agents, multimodal RAG |
| 11 | AI System Design | 10 case studies | A reusable answer framework plus eight worked case studies |
| 12 | Coding Challenges | 19 challenges | Implement attention, BPE, sampling, KV cache, an agent loop, and more, from scratch |
| 13 | Interview Process & Behavioral | 37 | Loop anatomy by company type, take-homes, portfolio projects, AI-specific behavioural questions |
Full worked examples in 11-ai-system-design/case-studies, each following the same template: requirements, architecture, evaluation plan, cost estimate, failure modes, and likely follow-ups.
Nineteen self-contained Python files in 12-coding-challenges, numpy and the standard library only, each with a reference solution and a real test suite. Read only the problem statement, implement it yourself, then run the file directly, for example python3 12-coding-challenges/01_attention.py.
| # | Challenge | Difficulty | Concepts |
|---|---|---|---|
| 01 | Attention | Medium | Scaled dot-product attention, multi-head, causal masking |
| 02 | BPE Tokenizer | Medium | Byte-level BPE: train, encode, decode |
| 03 | Sampling Strategies | Easy | Temperature, top-k, top-p, min-p, repetition penalty |
| 04 | Positional Encodings | Medium | Sinusoidal PE, RoPE |
| 05 | LayerNorm & Softmax | Easy | Stable softmax/log-softmax, LayerNorm, RMSNorm |
| 06 | KV Cache | Medium | Autoregressive decode loop with vs. without KV cache |
| 07 | Mini-GPT Forward Pass | Hard | Full GPT block forward pass: embeddings, attention, MLP, logits |
| 08 | Semantic Search / RAG | Medium | Embed, index, cosine retrieval, context assembly |
| 09 | Text Chunking | Easy | Fixed, sliding-window, recursive, sentence chunkers |
| 10 | Agent Loop | Medium | Tool-calling agent loop with a mock LLM |
| 11 | Rate Limiter & Retry | Medium | Token bucket, exponential backoff with jitter |
| 12 | Eval Metrics | Medium | pass@k unbiased estimator, QA F1/EM, judge harness skeleton |
| 13 | Streaming Parser | Hard | SSE parser, incremental tool-call argument assembly |
| 14 | LoRA Adapter | Medium | LoRA forward, merge, batched multi-adapter serving |
| 15 | Beam Search | Medium | Beam search with length normalisation |
| 16 | Semantic Cache | Medium | Exact and semantic tiers, TTL, LRU, tag invalidation |
| 17 | Hybrid Search & Rerank | Medium | BM25, reciprocal rank fusion, MMR |
| 18 | Constrained JSON Decoding | Hard | Schema-masked token decoding |
| 19 | Speculative Decoding | Hard | Draft-and-verify rejection sampling |
Corrections, new questions, and new case studies are welcome. See CONTRIBUTING.md for the format.
New questions, company coverage, and study material land here regularly. Watching the repo is the easiest way to catch every update.
Follow @ombharatiya for interview tips and updates, and see aidaddy.tech for the companion AI system design and interview prep site:
MIT.