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mlmentorship

About

mlmentorship

Senior ML & AI interview prep, by Hamidreza Saghir. Free.

Author

Hamidreza Saghir, Principal Applied Scientist at Microsoft. Earlier roles include Senior MLE Lead at X (Twitter), Applied Scientist at Amazon, and ML Researcher at Borealis AI (RBC). PhD from the University of Toronto, M.Sc. from Sharif University of Technology. Publications at ACL, InterSpeech, The Web Conference, IEEE TASLP, and Physical Review E.

I keep my opinion writing, consulting, and Looplet (an iterator-first agent framework) under my own name at hsaghir.com. mlmentorship is the focused interview-prep companion.

Why this site exists

Friends and ex-colleagues kept asking me the same questions about senior ML jobs and interviews: how to prep for the loop, how senior through senior-principal scope differs, what evals to build for an LLM product, how to scope an ambiguous applied-ML problem. After answering them in DMs and 1:1s a few too many times, I started writing the answers down. mlmentorship is the version I felt comfortable putting on the public web.

Public ML interview prep is calibrated for new grads and bootcamp-to-FAANG transitions. Free content for that audience is abundant.

Less common is content for mid-career ML practitioners targeting senior, staff, principal, and senior-principal roles. The questions are different, the bar is different, and the signal interviewers read is different.

This field guide contains opinionated, specific notes on what senior Applied Scientist, Research Scientist, MLE, and Research Engineer interviews actually test for, and on the topics that come up most often. Its private local Workbook turns those books into a focused practice plan.

What's here

  • Library: four shelves and ten books that combine concepts, questions, guides, and visual coding traces.
  • Questions: the senior ML canon, with what L4 / L5 / L6 answers sound like.
  • Workbook: a browser-local plan, next action, retry record, and simulation handoff.
  • Reading paths: optional front matter for goals that cross several books.
  • Guides: long-form pieces on senior through senior-principal ML interviews, system design, and patterns from teams that ship.
  • Concepts: 186 concise notes on ML, deep learning, evaluation, and systems.

What's not here

  • Generic ML basics. The internet has this covered.
  • New-grad / bootcamp / "break into ML" content.
  • Promises about landing a specific job in a specific timeframe.

Reach me

See how the site handles analytics, local Workbook data, and optional email subscriptions.