88.18 Machine Learning Interviews

Chapter 1. ML Roles and the interview process

ML Interview Process|800
ML Interview process

Skills Job titles
Data scientist (DS) ML Engineer
Data visualization, communication ★★★ ★★ ★★★
Data exploration, cleaning, intuition ★★★ ★★★ ★★★ ★★★
ML theory, statistics ★★★ ★★★ ★★
Programming tools (Python, SQL) ★★★ ★★★ ★★★ ★★★
Software infrastructure (Docker, Kubernetes, CI/CD)

★ to ★★★ ★★★
Skills to the Job titles

ML Lifecycle|800
ML Lifecycle

Common ML job titles and how they correspond to the ML lifecycle|800
Common ML job titles and how they correspond to the ML lifecycle.

The interview process can be shortcut with a strong referral.|800
The interview process can be a shortcut with a strong referral.

Chapter 2. ML Job Application and Resume

ML Job Application Guide


Applications × Effectiveness per application (EPA) → Interview invites

Job applications and their effectiveness per application|800
Job applications and their effectiveness per application

Asking for referral

Just a template:

Hope you are doing well. I saw that ABC is hiring for DEF position and also just noticed that you are working there.
I am curious to learn about your working experience at ABC and if you would recommend applying? Thanks.

  1. State a connection.

    • They stated where they had met me before. In some cases, job seekers mention reading my blog or seeing me speak.
    • They may mention something as simple as seeing one of my LinkedIn posts (it’s important to be specific about which one).
  2. Be specific.

    • They linked the job posting or mentioned details about why they were reaching out.
    • Sometimes I get very broad questions, such as “How do I enter data science?” In those situations, even if I have a coffee chat with them, I’ll be duplicating and repeating information that they could get in one of my blog posts, or from this book! A call or meeting should be meant for a deeper conversation.
  3. Politeness goes a long way.

    • They weren’t pushy or rude and were very respectful of my time.

A significant amount of hiring occurs through channels such as cold-emailing managers, warm introductions via referrals, or networking events.
In fact, I advise my mentees to never apply through the job board/company website unless it is absolutely necessary.
-Suhas Pai, CTO of Bedrock AI

Experience Writing

Here are some more tips to improve your initial bullet points:

Resume Resources

Table 2-2. Spreadsheet example of tracking applications and interviews

Application date Company Job posting URL Interview type Interview date Interviewers Emails Notes Results
2023-08-02 ARI Corp https://[url-to-job-description] Hiring manager: behavioral and past project deep dive 2023-08-15 Xue-La (hiring manager) [email protected] Recruiter says this is the ad revenue ML team Pending
2023-08-03 Taipaw AI https://[url-to-job-description] Recruiter screen 2023-08–5 Max (recruiter) [email protected] Asked about PyTorch exp Passed

Chapter 3. Technical Interview: Machine Learning Algorithms

As a rule of thumb, it’s vital to explain algorithms and ML concepts at two levels: on a simple “explain like I’m five years old” level and at a deeper, technical level, one more appropriate for a college course. A second rule of thumb is to be prepared to answer follow-up questions to these ML algorithm interview questions. This is so the interviewer knows that you didn’t just memorize and then regurgitate the answer but that you can apply it to various real-life scenarios on the job.

Read: https://huyenchip.com/ml-interviews-book/

Chapter 4. Technical Interview: Model Training and Evaluation

ML Task selection|400
Simplified ML Task selection

Chapter 5. Technical Interview: Coding

Overall, for Data scientists or ML Engineers, the questions are asked the following categories:



Data and ML Interview Questions

Here are some resources for further practicing data- and ML-related interview questions:


Here are some of the patterns to look out for:

Practice platforms for coding interviews

Curated study resources for coding interviews

The following are popular and useful guides for this type of question; basically, they are the same resources you’d use for a regular software engineer interview loop:

Curated practice problems for coding interviews

For more patterns, you can check resources such as the LeetCode 75 Study Plan for a full list of categories and read about them in resources such as the blog post “7 of the Most Important LeetCode Patterns for Coding Interviews” by Hunter Johnson.

Resources for SQL Coding Interview Questions

The example given here is for you to get an idea of basic questions in SQL that test on joins. However, more advanced tables may include more tables, more complex tables, window functions, subqueries, and so on. Use these resources to further your preparation:

Chapter 6. Technical Interview: Model Deployment and End-to-End ML

Kubernetes Books

In terms of implementation, here are some common tools for visualization and monitoring:

Tools for data checks or data unit tests include:

For greater depth on this subject, I recommend the following resources:


Also Read

Thoughts 🤔 by Soumendra Kumar Sahoo is licensed under CC BY 4.0