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Machine Learning Interview Preparation

A plan for machine-learning interviews: the target role and its rounds, fundamentals and maths, metric and model choices, coding with data, ML system design, project stories and research discussion, rehearsed in mocks.

Free · Mac (Apple silicon) and Windows · v1.0.0 · Other platforms

Then open the Marketplace in the app, search for Machine Learning Interview Preparation, and click Install. It goes into your own files, and works offline from then on.

What it installs.

1

Pillar

1

Area

50

Projects

200

Starter tasks

🏅 Tech & Engineering Certifications

Machine Learning Interview Preparation

Preparing for machine-learning interviews: ML fundamentals, model and metric choices, ML system design, coding with data, project stories and research discussion.

  1. Target ML role and interview format map
  2. ML interview skills baseline
  3. Twelve-week ML interview study plan
  4. ML concept notes and flashcard deck
  5. Python data stack refresher for interviews
  6. Linear algebra and calculus refresher for ML
  7. Probability and statistics refresher for ML interviews
  8. Core ML interview reading list
  9. ML project inventory with measured outcomes
  10. Twice-weekly ML study block routine
  11. Daily five-minute ML concept question
  12. Weekly coding-with-data practice set
  13. Twice-monthly ML system design mock
  14. ML interview mistake log and weekly review
  15. Weekly paper for research discussion rounds
  16. Peer mock interview exchange
  17. Monthly ML interview readiness score
  18. ML role application and round tracker
  19. Bias, variance and regularisation explained aloud
  20. Classic models explained from first principles
  21. Metric choice drills for imbalanced and ranking problems
  22. Deep learning training mechanics review
  23. Attention and transformer architecture walkthrough
  24. From-scratch ML implementations in NumPy
  25. Data leakage and feature pipeline scenarios
  26. Online experiments and A-B test reasoning
  27. ML system design answer framework
  28. LLM fine-tuning, retrieval and evaluation topics
  29. Choosing the two projects to present in depth
  30. Project walkthrough script with trade-offs and numbers
  31. Notebook project turned into a reviewable repository
  32. Four-week sprint on your weakest ML topic
  33. Choosing mock interview services and question banks
  34. Diagram set-up for remote ML design rounds
  35. ML fundamentals phone screen preparation
  36. Live data coding round rehearsal
  37. Onsite ML system design round preparation
  38. Research job talk for scientist interviews
  39. Final week before an ML onsite
  40. Software engineer moving into ML engineering
  41. PhD or postdoc move into industry research roles
  42. Data scientist move into ML engineer roles
  43. Senior and staff ML engineer interview preparation
  44. ML interview plan around a full-time job
  45. New graduate ML interview preparation
  46. Recommender and ranking system design practice
  47. Domain round preparation for vision, speech or NLP
  48. MLOps and model monitoring interview topics
  49. Responsible AI, fairness and safety questions
  50. Paper critique rounds for research scientist interviews

Where it starts.

Each project arrives with its purpose, milestones and first tasks written out. Here is how the first three read.

Target ML role and interview format map

Machine-learning job titles hide very different interviews: an ML engineer loop leans on coding and system design, an applied scientist loop on modelling judgement, and a research scientist loop on papers and a job talk. Writing down which role you want and which rounds your target companies actually run stops you preparing for an interview you will never sit.

First tasks

  • Write the role title you want and one you would also accept
  • List ten companies or teams hiring for that role
  • Ask two recent interviewees or a recruiter which rounds the loop contains
  • Tally the round types across your list and circle the top three

ML interview skills baseline

Few candidates know where they are weakest until an interviewer finds it for them. Scoring yourself across ML fundamentals, statistics, deep learning, coding with data, ML system design, project storytelling and research discussion, with a short test in each, gives the study plan a starting point you can measure against later.

First tasks

  • Copy the seven skill areas into a skills matrix with topics under each
  • Answer one question per topic aloud and score yourself honestly
  • Mark the three weakest topics that your target rounds will test
  • Save the dated baseline next to your study plan

Twelve-week ML interview study plan

Breadth is the trap in ML interviews: there is always another architecture or paper to read, and without a calendar the system design and coding rounds get squeezed out. A twelve-week plan, weighted by your baseline gaps and the rounds you mapped, gives every week a theme and a mock.

First tasks

  • Decide how many study hours a week you can genuinely protect
  • Assign your three weakest topics to weeks one to six
  • Put one mock of a different round type into each week
  • Block the study sessions in your calendar for the full twelve weeks

Make it yours.

Install Machine Learning Interview Preparation, then tell Aurum the date you're aiming for. It checks whether the plan fits the weeks you actually have.

Free · Mac (Apple silicon) and Windows · v1.0.0 · Other platforms

Blueprints are general information to help you organise your own work. They are not medical, financial, legal or other professional advice. See the Terms.