Machine Learning Engineer · Onsite · Multiple Types

Apple — Machine Learning Engineer ❌ Failed

Level: Senior-Level

Round: Onsite · Type: Multiple Types · Difficulty: 6/10 · Duration: 225 min · Interviewer: Neutral

Topics: Behavioral Questions, Dynamic Programming, Edit Distance, K-Means, NumPy, PyTorch, LLM, SFT, DPO, Machine Learning Fundamentals, System Design

Location: San Francisco Bay Area

Interview date: 2026-01-15

Got offer: False

Summary

Interview Rounds Overview

• Round 1: Behavioral
• Round 2: Coding
• Round 3: AI Coding
• Round 4: Machine Learning Fundamentals
• Round 5: System Design

Details

I had an onsite interview consisting of five rounds, each lasting 45 minutes.

In the behavioral round, I was asked about priority conflicts and what I would do if blocked by another team.

For the coding round, I had to solve a variation of the edit distance problem. The specific question was to check if two sentences differ by only one edit distance and return a boolean. While the general edit distance problem can be solved using dynamic programming, the constraint of edit distance being 1 allows for a more efficient solution, such as checking if the length difference is less than or equal to 1.

The AI coding round involved implementing k-means using NumPy or PyTorch. I needed to be familiar with the syntax (e.g., keepdim, unsqueeze) and broadcasting. The interviewer was looking for an efficient implementation rather than a naive for-loop version.

In the ML fundamentals round, the discussion covered LLMs, SFT, and DPO, from data and model aspects to evaluation.

The design round focused on designing Siri's response generation. I had to consider how to ensure the response is factual and concise, given that it's a voice chat interface.