Machine Learning Engineer applicants have rated the interview process at Apple with 3.3 out of 5 (where 5 is the highest level of difficulty) and assessed their interview experience as 54% positive. To compare, the company-average is 64% positive. This is according to Glassdoor user ratings.
Candidates applying for Machine Learning Engineer roles take an average of 43 days to get hired, when considering 39 user submitted interviews for this role. To compare, the hiring process at Apple overall takes an average of 29 days.
Common stages of the interview process at Apple as a Machine Learning Engineer according to 39 Glassdoor interviews include:
Phone interview: 25%
One on one interview: 23%
Skills test: 22%
Personality test: 10%
Presentation: 7%
Background check: 7%
Group panel interview: 3%
IQ intelligence test: 2%
Other: 2%
Here are the most commonly searched roles for interview reports -
The entire process stretched over three weeks, which felt a bit longer than I had anticipated. It kicked off with a technical phone screen where I tackled a binary tree traversal question. The next round involved implementing masked multi-head self-attention for a transformer, which I had worked through on PracHub in preparation. The final stage was a system design question comparing DCN v1 and v2, followed by a discussion on A/B testing. Overall, the experience was straightforward, and I was pleased to receive an offer, which I happily accepted.
Interview questions [3]
Question 1
Implement masked multi-head self-attention for a transformer from scratch
I applied online. I interviewed at Apple (Austin, TX) in Mar 2026
Interview
It was a 5 rounds of the interview. Interviewers were very polite and helpful. The questions were not too tricky. Leetcode part was easier than I thought. Focused more on basics.
An HR reached out and scheduled a BQ round immediately, talked to the hiring manager and focused on my resume in detail, then after that leetcode round and ml round