Machine Learning Engineer · Phone Screen · System Design

Apple — Machine Learning Engineer ✅ Passed

Level: Intern

Round: Phone Screen · Type: System Design · Difficulty: 6/10 · Duration: 60 min · Interviewer: Neutral

Topics: System Design, Machine Learning, Data Preprocessing, Model Selection, K-Means

Location: San Francisco Bay Area

Interview date: 2026-02-15

Summary

I had a phone screen for a Machine Learning Engineer role. After a brief introduction, the interviewer focused on my resume and then moved on to system design questions. I was given a specific application scenario, including the type of data and associated challenges, and asked to design a system covering data preprocessing, model selection, and training methods. Finally, I was asked to implement K-Means.

Details

The interviewer asked me to design a machine learning system, starting from data preprocessing to model selection and training. Then, I had to write the K-Means algorithm.

My approach:

1. I started by discussing the data preprocessing steps required for the given data type.
2. Next, I explored various model options, considering their pros and cons for this particular problem.
3. I outlined the training methodologies I would use.
4. Finally, I implemented the K-Means algorithm.

Key insights:

• Understanding the data type is crucial for choosing the right preprocessing techniques.
• Different models have different strengths and weaknesses, and the best choice depends on the specific problem.
• Proper training methodologies are essential for achieving good model performance.