AIMLInterviews: A Markdown Curriculum Plus an MCP Tutor for AI/ML Interview Prep
This repo is meant to serve as a guide for Machine Learning/AI technical interviews.
At a glance
- What is it?
- AIMLInterviews is an MIT-licensed Jupyter Notebook repository that maps AI/ML interview loops into six chapters and ships an MCP server so an AI assistant can quiz you from the same curriculum. It is a study guide with a coaching funnel attached, not a course platform or a benchmark suite.
- Who is it for?
- Adopt AIMLInterviews if you are targeting AI/ML engineering, applied science, or tech lead loops at large companies and want a chapter-ordered reading list plus an MCP tutor you can point at your own clone. Do not adopt it if you need a graded course with answer keys, verified solutions, or coverage of startup-specific interviews, since the README states startup interviews are often tailored and the material is compiled from one author's notes.
- Can I use it commercially?
- Yes. MIT is a permissive licence: you can use, modify and sell software built on it, as long as you keep its copyright and licence notices.
- Is it still maintained?
- Yes. The repository last received commits 14 days ago.
- What is it written in?
- Mainly Jupyter Notebook, according to GitHub's language statistics.
Answers come from the project's GitHub data, last synced on September 15, 2026, and from our analysis. They are not legal advice.
DEEP OPEN-SOURCE ANALYSIS
The Gap AIMLInterviews Fills: A Loop Map Instead of a Question Dump
Most interview prep repositories are question lists. AIMLInterviews is organized as a loop map. The README splits preparation into six named chapters: General Coding and DSA, ML Coding, ML Fundamentals and Breadth, ML/GenAI/LLM System Design, Agentic AI Systems, and Behavioral Interviews. Each chapter links to a file inside the repository, for example src/lc-coding.md, src/MLC/ml-coding.md, src/ml-fundamental.md, and src/MLSD/ml-system-design.md. The Agentic AI chapter is the exception: it points at a separate repository, alirezadir/Agentic-AI-Systems.
The stated audience is narrow and worth taking literally. The README says the guide focuses on AI/ML Engineering, Applied Science, and Tech Lead roles at big companies, with FAANG named explicitly. It also states that Data Science and Research Scientist loops have different structures, and that some modules here can still be useful for them. That is a boundary, not a disclaimer. If your target title is Data Scientist, the chapter ordering will not match your loop.
The provenance is one person's preparation notes. The README attributes the compilation to the author's own interview preparation, describing five simultaneous offers in 2020 and repeat offers in 2025. That origin explains both the strength (the structure mirrors a real loop) and the weakness (coverage depth depends on what one candidate happened to study).
What the MCP Server Actually Does With the Curriculum
The distinguishing feature is the AI tutor. The README describes an MCP server, aimlinterviews-mcp, that turns an MCP-compatible assistant into an interview coach. Four capabilities are listed: discovering curriculum problems, offering progressive hints, building learning and company preparation plans, and reviewing answers in what the README calls a no-spoilers teaching style.
The mechanism is file-based, and the README is explicit about it. The server reads the repository content, so you clone the repository first, then run the registration command either from the clone or with an environment variable pointing at it. The README names that variable: AIMLINTERVIEWS_ROOT. The MCP guide at MCP/README.md is where configuration, tools, and development instructions live; the top-level README does not enumerate the tool names or their schemas.
That is a meaningful design choice. The tutor is not a hosted service with its own question bank. It is a retrieval layer over Markdown files you already have on disk. The practical consequence: the quality of hints and answer reviews tracks the quality of the chapter files, and if you edit those files, you change what the tutor can draw on. It also means the no-spoilers behavior is a prompting and tooling decision inside the server, not a property of the content itself.
Getting It Running: Two Commands and One Environment Variable
The README gives two registration commands, one per client. For Claude:
claude mcp add aimlinterviews -- npx -y aimlinterviews-mcp
For Codex:
codex mcp add aimlinterviews -- npx -y aimlinterviews-mcp
Both invoke the package through npx with the -y flag, which means the server is fetched from the npm registry at run time rather than vendored into the repository. The prerequisite stated in the README is that you clone the repository first, then run the command from the clone or set AIMLINTERVIEWS_ROOT to point at it.
The repository itself is mostly Markdown and Jupyter Notebook content, so there is no build step, no dependency install, and no test suite described in the README. The only executable surface is the MCP server, and the README defers its configuration details to MCP/README.md. If you want to know which tools the server exposes, what arguments they take, or how to run it in development mode, that file is the place to look, and this review cannot confirm its contents.
One operational detail to note: because the server is pulled via npx, the version you get is whatever npm resolves at that moment, not a pinned commit. The repository has no releases retrieved, so there is no tagged version to pin against.
Where the Guide Is Thin: No Answer Keys and a Single-Author Bias
The most concrete limitation is structural. The repository is a guide, and the README describes it as compiled from personal experience and notes. Nothing in the material indicates that the chapters contain worked solutions with verification, grading rubrics, or difficulty ratings. For DSA and ML coding chapters, that matters: a problem list without reference solutions shifts the burden of correctness onto you or onto an assistant reviewing your attempt.
The MCP tutor partly addresses this, but it inherits the same base. The README describes answer review and progressive hints, and the no-spoilers style is deliberate. Whether the review is accurate depends on the underlying files and the model doing the reviewing. There is no stated evaluation of hint quality.
Translation coverage is another asymmetry. The README header lists English, Simplified Chinese (README-CN.md), and Persian (README-FA.md). Those are README translations. The README does not state that the chapter files under src/ are translated, so a reader who needs Chinese or Persian chapter content should check the individual files rather than assume parity.
Finally, the scope boundary repeats here. The README states that startup interviews are often tailored to their own use cases and problems, while larger companies follow a more consistent structure. If you are interviewing at a startup, the six-chapter FAANG-shaped map is the wrong instrument.
How It Differs From Generic Interview Question Banks
The obvious alternative is a large community question bank such as the well-known DSA problem sets, or a paid course platform. The difference is in what is being organized. A question bank organizes by problem; AIMLInterviews organizes by interview module and then points each module at a file. That makes it better for planning a study sequence across a loop and worse for grinding a high volume of graded problems.
A second alternative is the author's own separate Agentic-AI-Systems repository, which the README lists as Chapter 5. That is not a competitor so much as a split: agentic content lives outside this repo, so a clone of AIMLInterviews alone does not give you Chapter 5. You need the second repository, or you follow the link.
A third comparison is the MCP server against simply pasting chapter text into a chat window. The server adds discovery (finding problems in the curriculum), plan building (learning and company preparation plans), and a stated no-spoilers review style. The manual approach gives you none of that structure but also does not depend on an npm package resolving correctly or on AIMLINTERVIEWS_ROOT being set. If the npx invocation fails in your environment, the fallback is reading the Markdown, which is the entire content anyway.
Licence and the Cost of Keeping a Clone Current
The repository is MIT licensed, per both the README badge and the LICENSE file reference. MIT permits commercial and private use, modification, and redistribution with the licence and copyright notice retained. This is not legal advice; if you plan to redistribute the content or fold it into a product, read the LICENSE file and, for anything consequential, consult counsel. The separate Agentic-AI-Systems repository is a different codebase with its own licence, which the README does not state here.
Maintenance cost is low but not zero. There is no package to upgrade and no build to break; the content is Markdown and notebooks. The moving part is the MCP server, which is installed through npx and therefore tracks the published package rather than a pinned revision. The repository has no releases retrieved, so there is no version tag to hold. The README notes the repository was updated for 2026 with expanded LLM, multimodal AI, post-training, and GenAI system-design content, and the last push date is recent, which suggests active editing. For a study guide, that is the right shape: you pull, you read, you move on. The failure mode is drift between the chapter files and the MCP server's expectations if the two are updated out of step, which is the thing to watch when you pull.
Who Should Clone This, and What to Check First
The fit is specific. You are preparing for an AI/ML engineering, applied science, or tech lead loop at a large company, you want a module-by-module reading plan rather than a problem feed, and you are willing to supply your own discipline about working through the chapters. The MCP server is a genuine differentiator if you already use an MCP-compatible assistant, because it turns the repository into an interactive coach without a separate subscription.
The misfit is equally specific. If you need graded problems with verified solutions, this is not that. If your loop is Data Science or Research Scientist, the README itself says the structure differs. If you are targeting a startup, the README says those interviews are tailored, so a FAANG-shaped map helps less. And if you cannot run the MCP server in your client, you are left with a Markdown curriculum, which is still useful but is not the feature being advertised.
Three things to verify before committing study time. First, run claude mcp add aimlinterviews -- npx -y aimlinterviews-mcp (or the codex equivalent) and confirm the server registers. Second, confirm that AIMLINTERVIEWS_ROOT points at your clone if you are not running from inside it. Third, open the chapter files themselves, starting with src/ml-fundamental.md and src/MLSD/ml-system-design.md, and judge whether the depth matches your loop, because the README describes the structure but not the depth.
Editorial conclusion
Adopt AIMLInterviews if you are targeting AI/ML engineering, applied science, or tech lead loops at large companies and want a chapter-ordered reading list plus an MCP tutor you can point at your own clone. Do not adopt it if you need a graded course with answer keys, verified solutions, or coverage of startup-specific interviews, since the README states startup interviews are often tailored and the material is compiled from one author's notes. Before relying on it, verify three things: that the MCP server starts with your client using the documented npx command, that AIMLINTERVIEWS_ROOT resolves to your clone, and that the chapter files under src/ actually contain the depth you need for the specific loop you are preparing for.
Community notes