Machine Learning Engineer · Onsite · Multiple Types

Apple — Machine Learning Engineer ❌ Failed

Level: Senior-Level

Round: Onsite · Type: Multiple Types · Difficulty: 6/10 · Duration: 300 min · Interviewer: Friendly

Topics: Machine Learning, Natural Language Processing, System Design, Behavioral Questions

Location: San Francisco Bay Area

Interview date: 2025-12-15

Got offer: False

Summary

Interview Rounds Overview

• Round 1: Phone Screen
• Round 2: Coding
• Round 3: Domain Knowledge
• Round 4: System Design
• Round 5: Behavioral (includes SD)

Details

The interview process consisted of a phone screen and four onsite rounds.

Phone Screen: I was asked about implementing a bag of words model, out-of-bag (OOB) structure, domain knowledge, and a binary model for predicting click-through rate (CTR). I was also asked how to check performance if online performance decreases.

Onsite Rounds:

• Coding: I was asked questions similar to leetcode questions (like stock prices problem and a monkey eating bananas problem).
• Domain Knowledge: I was asked to describe all the steps and techniques involved in building a classification model, including model selection.
• System Design: I had to design the app store search functionality and Apple News, assuming there was no model for search. The interviewer wanted me to design a basic system.

LeetCode similar: LeetCode 121, LeetCode 875