AIMLInterviews — AI/ML Interview Prep Guide
A structured AI/ML technical interview prep guide covering coding, ML fundamentals, GenAI system design, and behavioral rounds, with an MCP AI-tutor server.
Evidence shows a documentation-first interview guide repo with an MCP catalog server covered by tests. Tests verify repository reads cannot escape the configured root (partial least_privilege evidence) and actively strip reference answers (SECRET_REFERENCE_IMPLEMENTATION / SECRET_INLINE_ANSWER), supporting sensitive_data_handling. No evidence of user confirmation, rollback, or external-effect mechanisms — those score 0. Author attribution (Alireza Dirafzoon, MIT copyright) is clear. No dependency manifest appears in the provided files, so dependency_security cannot be verified.
Tests demonstrate stable ID generation (legacy ID compatibility, deterministic repeated builds) and a clear error message for path escape (/outside repository root/), earning failure_messages credit. Dependency availability is unverifiable — no package. or lockfile provided.
The README clearly defines the target audience (ML engineering/applied-scientist roles at FAANG-style large companies) and notes that startup interviews differ, supporting audience_and_scenarios and capability_boundaries. However, trigger precision and environment fit (Node version, installation environment) for the MCP product are essentially undocumented.
Information architecture is clear (six chapters, multilingual READMEs, cn/ directory), naming is stable (repo name, aiml:problem: ID prefix), and the full MIT license is included. Missing: install notes for the MCP server, any changelog/version number (only a verbal '2026 edition' claim), and documented maintenance responsibility or update path.
Output is a structured catalog (stable IDs, difficulty, company tags) with clear marginal value for interview prep; tests show it is machine-consumable. Cost-benefit is reasonable — static content plus a lightweight MCP server. Actual usability depends on MCP server implementation files not provided.
The author's personal offer claims (5 offers in 2020, Amazon/Apple 2025) cannot be traced within the repo; claim_traceability is weak. Question difficulty and company tags are single-sourced from author notes with no cross-corroboration. Separation of facts from marketing-style claims is mediocre.
- The MCP server portion lacks install instructions, a dependency manifest, and Node version requirements — verify independently before deployment.
- No version number or changelog exists; the '2026 edition' is only a verbal README claim.
- The author's personal offer history is an unverifiable personal claim and should not be treated as evidence of content quality.
- Difficulty and company tags are single-sourced; cross-check against official channels for actual interview prep.
- Publisher is unverified; review content freshness before using this repo as an Agent data source.
What does this agent do, and when should you use it?
AIMLInterviews is an MIT-licensed open-source repository that guides candidates through AI and ML technical interviews at big tech companies, particularly FAANG. Its content is compiled from the author's own preparation notes, drawn from five simultaneous offers in 2020 (Meta ML Specialist, Google ML Engineer, Amazon Applied Scientist, Apple Applied Scientist, Roku ML Engineer) and repeat offers from Amazon and Apple in 2025 (AI Tech Lead). The guide is organized into chapters: general coding/DSA, ML coding, ML fundamentals (classic ML, LLMs, multimodal AI), ML/GenAI/LLM system design, Agentic AI systems, and behavioral interviews, plus GenAI learning resources and downloadable behavioral/leadership prep worksheets in Google Sheets and Excel. Beyond static content, an MCP server (aimlinterviews-mcp) turns any MCP-compatible AI assistant into an interview coach that discovers curriculum problems, offers progressive no-spoiler hints, builds study and company-prep plans, and reviews answers. The README is available in English, Simplified Chinese, and Farsi. Note this is a learning guide and tutoring tool, not a production agent product.
The repository delivers interview curriculum as Markdown chapters: Chapter 1 general coding/DSA (src/lc-coding.md), Chapter 2 ML coding (src/MLC/ml-coding.md), Chapter 3 ML fundamentals (src/ml-fundamental.md, covering classic ML, LLMs, and multimodal AI), Chapter 4 ML/GenAI/LLM system design (src/MLSD/ml-system-design.md), Chapter 5 Agentic AI systems (linked to a separate alirezadir/Agentic-AI-Systems repository), and Chapter 6 behavioral interviews (src/behavioral/behavior.md) with an accompanying Google Sheets worksheet and Excel download. Additionally, the aimlinterviews-mcp server in the MCP directory runs via npx and can be registered with clients like Claude or Codex: it discovers curriculum problems, provides progressive hints in a no-spoilers teaching style, builds learning and company preparation plans, and reviews user answers.
- A candidate preparing for an ML Engineer, Applied Scientist, or AI Tech Lead role at a FAANG company who wants to review module by module across system design, ML fundamentals, and coding.
- An engineer targeting GenAI or Agentic AI positions who needs dedicated material on LLM, multimodal, and agent system design.
- A learner with an MCP-compatible AI assistant (e.g., Claude or Codex) who wants to configure it as an interview coach with progressive hints and answer review.
- A senior engineer or manager prepping behavioral and leadership rounds using the bundled Google Sheets/Excel preparation templates.
- An AI/ML practitioner seeking 1:1 coaching or mock interviews, available through the author's paid coaching service at aimlinterviews.io.
What are this agent's strengths and limitations?
- Content is grounded in the author's real interview outcomes — offers from Meta, Google, Amazon, and Apple — giving it a practitioner's perspective.
- Comprehensive coverage: DSA, ML coding, LLM/multimodal system design, Agentic AI, and behavioral rounds in one place.
- Updated for 2026 with expanded LLM, multimodal, post-training, and GenAI system-design content, and offered in English, Chinese, and Farsi.
- The MCP server provides a structured teaching flow (problem discovery, progressive hints, planning, answer review) that plugs into existing AI workflows.
- The guide targets AI/ML Engineering, Applied Science, and Tech Lead roles at big companies; Data Science and Research Scientist interviews follow different structures, so it only partially applies.
- The MCP AI tutor requires an MCP-compatible client (Claude or Codex), an npx runtime, and a cloned local copy — a higher setup bar than plain reading.
- Chapter 5 (Agentic AI Systems) lives in a separate external repository and must be fetched separately.
- There is no independent evidence of the AI tutor's teaching effectiveness; the README provides no evaluations or user feedback data.
How do you install or deploy this agent?
1) Clone the repo: git clone https://github.com/alirezadir/AIMLInterviews.git. 2) For the AI tutor, clone the repository first, then run one of the registration commands from inside the clone (or set AIMLINTERVIEWS_ROOT to the clone path): claude mcp add aimlinterviews -- npx -y aimlinterviews-mcp, or codex mcp add aimlinterviews -- npx -y aimlinterviews-mcp. 3) For content-only study, no installation is needed — read the Markdown chapters directly. See MCP/README.md for detailed configuration, tools, and development instructions.
How do you use this agent?
Content study: read through the chapter table in the README, opening the linked Markdown files (e.g., src/MLSD/ml-system-design.md), and fill out the behavioral interview prep worksheet as you practice. AI tutor: after registering the MCP server, invoke the aimlinterviews server within a Claude or Codex session; it discovers curriculum problems, gives progressive no-spoiler hints, generates study and company-specific prep plans, and reviews your answers. Coaching: visit https://aimlinterviews.io for 1:1 coaching and mock interviews.