The phone screen was a mix of behavioral questions and some basic technical concepts, which was a bit different from what I anticipated. After that, I faced a technical round that focused on implementing sparse matrix operations. To my surprise, the coding question was nearly identical to what I'd practiced in the algorithm section on PracHub just days before. The onsite interview included some more DSA challenges and a discussion on machine learning concepts, which helped me feel well-prepared. Overall, the experience was smooth, and I accepted the offer afterward.
Interview questions [1]
Question 1
Implement sparse matrix storage, addition, and multiplication
The assessment time was very short and harsh. There were lots of questions with a very limited time. I liked the other big companies' assessments better. For example, Meta and Google assessments are through talking to a person while coding.
I applied for the new grad MLE position. The process included an online assessment, a 20-minute HR call, and four onsite interviews (two coding and two machine learning system design).
Pinterest interviews FAQs
Machine Learning Engineer applicants have rated the interview process at Pinterest with 3.3 out of 5 (where 5 is the highest level of difficulty) and assessed their interview experience as 43% positive. To compare, the company-average is 49.2% positive. This is according to Glassdoor user ratings.
Candidates applying for Machine Learning Engineer roles take an average of 19 days to get hired, when considering 30 user submitted interviews for this role. To compare, the hiring process at Pinterest overall takes an average of 24 days.
Common stages of the interview process at Pinterest as a Machine Learning Engineer according to 30 Glassdoor interviews include:
Phone interview: 36%
One on one interview: 25%
Skills test: 23%
Presentation: 5%
Background check: 2%
IQ intelligence test: 2%
Personality test: 2%
Group panel interview: 2%
Other: 2%
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