Initial Leetcode round with a few ML related questions about LLMs, then onsite was 2 more leetcode rounds and 2 systems design interviews. This was genuinely one of the worst interview experiences I've ever had. The interviewers feel like they would rather be anywhere but there. Many of them were incredibly rushed and one had an office room rented that he was getting kicked out of so he just hung up the call without any time for questions or even a good bye. The worst part was one of the interviewers was actively trying to trick me. I said the right answer and then she would say "are you sure", I would reiterate yes and then she would say "are you reallyyy sure" until i second guessed myself and changed my answer since if someone says that, it must be wrong, but then she would be like ha wrong! Stay as far away as possible, they put the least amount of effort into the interview process of any company I've ever interviewed with, the questions are lazy and there's no training for the interviewers, clearly.
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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