Machine Learning Engineer · Onsite · Multiple Types

Apple — Machine Learning Engineer ❌ Failed

Level: Senior-Level

Round: Onsite · Type: Multiple Types · Difficulty: 6/10 · Duration: 240 min · Interviewer: Unfriendly

Topics: Machine Learning, System Design, Cache, Transformer Architecture, Agent, Meta-Cognitive Prompting (MCP), Prompt Engineering, Knowledge Distillation, Activation Functions, Sentiment Analysis, Topic Modeling

Location: San Francisco Bay Area

Interview date: 2026-01-10

Got offer: False

Summary

Interview Rounds Overview

• Round 1: ML Foundation
• Round 2: Past Projects
• Round 3: ML System Design
• Round 4: Engineering Experience + Code

Details

I had an onsite interview with four rounds. The recruiter wouldn't disclose the interview content beforehand.

Round 1: ML Foundation The interviewer mostly responded with "Ok" to my answers, leading me to doubt my performance. The questions covered traditional ML, evaluation metrics, transformer architecture, agents, MCP, prompt engineering, knowledge distillation, and activation functions. I realized I should ask the interviewer for feedback on whether my explanations were clear in future interviews.

Round 2: Past Projects This interviewer was enthusiastic and warned me that he would interrupt to signal he had enough information. Questions focused on how I approached problems, alternative solutions, validation methods, metrics, and data-driven decision-making. I felt this round went well, but it wasn't enough to pass.

Round 3: ML System Design The question was similar to a project I had worked on: sentiment and topic modeling applied to Apple's domain. I spent most of the time talking and occasionally asked the interviewer for questions. I need to lead the conversation more and proactively address topics instead of waiting to be asked. The design involved handling emerging topics and selecting appropriate models with justifications.

Round 4: Engineering Experience + Code The interviewer was friendly and said my answers to general engineering questions exceeded expectations. The coding question wasn't from LeetCode. The coding problems at Apple are very long, and I needed to extract a LeetCode-like problem from it. This round was about cache handling. I struggled to understand what the interviewer wanted initially, and I ran out of time. I received several hints, such as how to define the expiration of cache TTL and when to delete entries, and my approach differed from the interviewer's expectation. I found this coding round challenging.