💡 Preparing for ML Job Interviews: Insights from Yuri Kashnitsky's Journey

[Rudro]

1. Behavioral Interviews (13.5 Interviews)

Investing time in creating a story bank and participating in mock interviews is crucial. Aim to spend around 20-30 hours on this aspect.

• Resource Highlights:
  • IGotAnOffer Blog: Covers everything from MLE interview questions at Meta to "Why Amazon?" and how to discuss failures and conflicts.
  • Jackson Gabbard's Video: Watch here for insights into the importance of behavioral interviews.
  • Interviewing.io Guide: Check it out for a loose interpretation of Amazon Leadership Principles.
  • Mock Interviews: Practice is key. Use platforms like Interviewing.io for detailed feedback.

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2. Coding Interviews (8.5 Interviews)

Live-coding can be challenging, requiring you to think, code, listen, and talk simultaneously. Practice is essential.

• Resource Highlights:
  • Neetcode Roadmap by Navdeep Singh and Leetcode Premium: Comprehensive guides to coding problems.
  • Mock Interviews: Use Interviewing.io or Pramp to hone your skills.

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3. ML Breadth (6 Interviews)

Refresh your basics and dive deep into specific ML topics.

• Resource Highlights:
  • mlcourse.ai: Covers bias-variance decomposition, boosting vs. bagging, and gradients in gradient boosting.
  • NLP For You by Elena Voita: An excellent resource for NLP enthusiasts.
  • Jay Alammar's Posts: Great for understanding transformer architecture.
  • Illustrated ML and Daily Dose of Data Science: Helpful for daily learning.
  • Chip Huyen's "Machine Learning Interviews": Explore here for a comprehensive guide.

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4. ML Depth (5 Interviews)

Leverage your work experience and read relevant blogs.

• Resource Highlights:
  • Evidently AI's Collection of 300 Blogs: Browse here. Read 2-3 blogs from companies you're interviewing with and 2-5 relevant to the job description.

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5. ML Coding (4 Interviews)

Prepare through mock interviews.

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6. Research Presentation (4 Interviews)

Align with HR to understand their expectations regarding theory, engineering, etc.

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7. ML Systems Design (3.5 Interviews)

Learn the structured approach to ML systems design.

• Resource Highlights:
  • Helpful Repo: Explore here for clear response templates and typical cases.

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8. Take-Home Assignments (3 Assignments)

While the necessity of take-homes is debated, they can be highly educational.

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9. System Design (0.5 Interviews)

Devote time to understanding system design principles.

• Resource Highlights:
  • Interviewing.io Guide: Read here.
  • Primer: Check here for a classic resource.
  • "System Design Interview" Book: A quick read with many diagrams.
  • Neetcode System Design Course: Enroll here.

Note:  By following this structured preparation guide, you can significantly enhance your readiness for ML job interviews. Dive into these resources, practice diligently, and best of luck in your journey towards securing your dream job in machine learning! 🚀 This post was made with respect to the original post from Yuri Kashnitsky : https://www.linkedin.com/posts/kashnitskiy_resources-that-helped-me-in-my-48-interviews-activity-7210627927418245120-c2qp/ .

#MLInterviews #JobPreparation #MachineLearning #CareerGrowth

[Aman]

This is an excellent post, @Rudro! A lot of hidden gems in there 🙂 Thanks for sharing.